Target Tracking with Binary Proximity Sensors Fundamental Limits Minimal Description and Algorith

Target Tracking with Binary Proximity Sensors  Fundamental Limits Minimal Description and Algorith
Target Tracking with Binary Proximity Sensors  Fundamental Limits Minimal Description and Algorith

Target Tracking with Binary Proximity Sensors: Fundamental Limits,Minimal Descriptions,and Algorithms?

N.Shrivastava Dept.of Computer Science

Univ.of California Santa Barbara,CA93106,USA nisheeth@https://www.360docs.net/doc/251745843.html, R.Mudumbai U.Madhow

Dept.of Elec.&Comp.Engr.

Univ.of California

Santa Barbara,CA93106,USA

{madhow,raghu}@https://www.360docs.net/doc/251745843.html,

S.Suri

Dept.of Computer Science

Univ.of California

Santa Barbara,CA93106,USA

suri@https://www.360docs.net/doc/251745843.html,

Abstract

We explore fundamental performance limits of tracking a target in a two-dimensional?eld of binary proximity sensors, and design algorithms that attain those limits.In particular, using geometric and probabilistic analysis of an idealized model,we prove that the achievable spatial resolutionΔin localizing a target’s trajectory is of the order of1ρR,where R is the sensing radius andρis the sensor density per unit area. Using an Occam’s razor approach,we then design a geomet-ric algorithm for computing an economical(in descriptive complexity)piecewise linear path that approximates the tra-jectory within this fundamental limit of accuracy.We em-ploy analogies between binary sensing and sampling theory to contend that only a“lowpass”approximation of the tra-jectory is attainable,and explore the implications of this ob-servation for estimating the target’s velocity.

We show through simulation the effectiveness of the ge-ometric algorithm in tracking both the trajectory and the ve-locity of the target for idealized models.For non-ideal sen-sors exhibiting sensing errors,the geometric algorithm can yield poor performance.We show that non-idealities can be handled well using a particle?lter based approach,and that geometric post-processing of the output of the Particle Filter algorithm yields an economical path description as in the idealized setting.Finally,we report on our lab-scale ex-periments using motes with acoustic sensors to validate our theoretical and simulation results.

?This work was supported by the National Science Foun-dation under grants ANI-0220118,CCF-0431205,CNS-0520335, CCF0514738,the Of?ce of Naval Research under grant N00014-06-0066,and by the Institute for Collaborative Biotechnologies through grant DAAD19-03-D-0004from the U.S.Army Research Of?ce.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for pro?t or commercial advantage and that copies bear this notice and the full citation on the?rst page.To copy otherwise,to republish,to post on servers or to redistribute to lists,requires prior speci?c permission and/or a fee.

SenSys’06,November1–3,2006,Boulder,Colorado,USA.

Copyright2006ACM1-59593-343-3/06/0011...$5.00Categories and Subject Descriptors

H.1.1[Systems and Information Theory]:Information theory,Value of information

General Terms

Algorithms,Theory

Keywords

Sensor Networks,Target Tracking,Binary Sensing,Fun-damental Limits,Distributed Algorithms

1Introduction

We investigate the problem of target tracking using a net-work of binary proximity sensors:each sensor outputs a1 when the target of interest is within its sensing range,and 0otherwise.This simple sensing model is of both funda-mental and practical interest for several reasons.First,be-cause of the minimal assumption about the sensing capa-bility,it provides a simple and robust abstraction for a ba-sic tracking architecture of broad applicability,which can be enhanced in a situation-speci?c fashion to take advan-tage of additional information such as target velocity or dis-tance,if available.Second,the communication requirements for the binary proximity model are minimal—each sensor can smooth out its noisy observations and express its out-put as one or more disjoint intervals of time during which the target is in its range,which can be encoded ef?ciently by timestamps when the output changes from0to1and vice versa.Finally,the simplicity of the model permits the deriva-tion of intuitively attractive performance limits,which serve both to guide design of tracking algorithms and to provide lower bounds on tracking performance with more sophisti-cated sensors.

We begin by exploring the fundamental limit of spatial resolution that can be achieved in tracking a target within a two-dimensional?eld of binary proximity sensors.The spa-tial resolution measures the accuracy with which a target’s trajectory can be tracked,and it is de?ned as the worst-case deviation between the estimated and the actual paths.We prove that the ideal achievable resolutionΔis of the order of 1

ρR

,where R is the sensing range of individual sensors andρis the sensor density per unit area.This result articulates the common intuition that,for a?xed sensing radius,the accu-racy improves linearly with an increasing sensor density.But

it also shows that,for a?xed number of sensors,the accu-racy improves linearly with an increase in the sensing radius, which occurs because an increase in the sensing radius leads to a?ner geometric partition of the?eld.Our spatial reso-lution theorem helps explain empirical observations reported in prior work on tracking in binary sensor networks[12].

Next,we consider minimal representations and velocity estimation for the target’s trajectory.There are in?nitely many candidate trajectories consistent with the sensor ob-servations and within the guaranteed spatial resolution of the true trajectory,and all of which are“good enough”for local-ization accuracy.On the other hand,the velocity estimation for the target depends crucially on the shape of the trajectory. We use an analogy between binary sensing and the sampling theory and quantization to argue that“high-frequency”vari-ations in the target’s trajectory are invisible to the sensor?eld at a spatial scale smaller than the resolutionΔ.Therefore,we can only hope to estimate the shape or velocity for a“low-pass”version of the trajectory.1We then consider piecewise linear approximations to the trajectory that can be described economically.We give suf?cient conditions for the lowpass version of the true target trajectory under which such mini-mal representations can estimate the velocity accurately.

Our results on velocity estimation can be paraphrased as follows:velocity estimates for a segment of the trajectory approximated by a straight line are good if the segment is long enough.This motivates an Occam’s razor approach for describing the trajectories in terms of piecewise linear paths in which the line segments are as long as possible,without exceeding the approximation error limit provided by our spa-tial resolution theorem.We develop the O CCAM T RACK al-gorithm,which ef?ciently computes such piecewise linear trajectories,and associated velocity estimates,from the sen-sor observations.The ef?cacy of the algorithm in achieving the fundamental limits on spatial resolution and velocity es-timation error is demonstrated via simulations.

Next,we consider more realistic sensor models,in which the coverage areas for different sensors may be different,and not exactly known to the tracker node.For such non-ideal sensors exhibiting sensing errors,the O CCAM T RACK algo-rithm can yield poor performance.We show that sensor non-idealities can be handled well using a particle?lter approach adapted to non-ideal binary sensing.While this particle?l-ter algorithm is robust to nonidealities,the paths it outputs are not smooth,and therefore not easy to describe economi-cally.We show that geometric post-processing of the output of the particle?ltering algorithm leads again to minimal rep-resentations in terms of piecewise linear trajectories with a small number of line segments,with the required goodness of?t guided by the fundamental limits on spatial resolution. Simulations are used to demonstrate the effectiveness of the overall algorithm in providing accurate tracking with mini-mal path representations.

Finally,we carried out a lab-scale demonstration with motes equipped with acoustic sensors for a quick validation of our framework.We found that the coverage area varies 1A technical de?nition of the lowpass trajectory is given in Sec-tion3.4.1.signi?cantly across sensors,and exhibits nonmonotonicity: the probability that a target is detected does not necessarily go down monotonically with distance from the sensor.We employed two approaches to deal with real-world noisy data: (i)preprocessing of the noisy sensor outputs to clean up ob-vious error patterns,followed by the O CCAM T RACK algo-rithm,and(ii)the Particle Filter algorithm followed by ge-ometric post-processing.Both these approaches show good tracking performance.

Related Work

Object tracking has long been an active area of research for battle-?eld[9],robotics[16]and other applications.Any sensor that generates a signal dependent on distance from a target can be used for tracking.Accordingly different sens-ing modalities such as radar,acoustic,ultrasonic,magnetic, seismic,video,RF and infrared[15],and occasionally com-binations of multiple modalities[3],have been considered for tracking applications in both theory and practice.The range and capabilities of these sensors vary widely,and many different approaches to modeling and data processing have been investigated.For instance,ultrasonic signals carry rich information about the range of the object,whereas infrared sensors are best modeled as detectors or binary sensors[17]. We do not attempt to do justice to the vast literature on track-ing,but brie?y review closely related work.

The robustness and effectiveness of tracking using binary sensing models has been convincingly demonstrated for a large-scale sensor network in[1].The success of this project provides strong motivation for the fundamental exploration of binary sensing undertaken here.The authors of[12] consider a model identical to ours.They employ piece-wise linear path approximations computed using variants of a weighted centroid algorithm,and obtain good tracking per-formance if the trajectory is smooth enough.Our funda-mental limits provide an explanation for some of the empiri-cal observations in[12],while our algorithms provide more accurate and more economical path descriptions.Another closely related paper is[2],which considers a different sens-ing model,where sensors provide information as to whether a target is moving towards or away from them.While the speci?c results for this model are quite different from ours, the philosophy is similar in that the authors of[2]use geo-metric analysis to characterize fundamental limits.However, the sensing model in[2]can lead to unacceptable ambigui-ties in the target’s trajectory(the authors offer an example of parallel trajectories that are indistinguishable without addi-tional proximity information).In contrast,the binary prox-imity model considered here,despite its minimalism,is able to localize the target to within a spatial resolution O(1ρR). In[13],Liu et al.present some interesting ideas using geo-metric duality to track moving shadows in a network of bi-nary sensors.Although their technique is not applicable to our problem setting,their notion of cells in dual space has some resemblance to our localization patches.

Classical tracking is often formulated as a Kalman?l-tering problem,using Gaussian models for sensor measure-ments and the target trajectory.Distributed tracking based on Kalman?ltering has recently been considered in[14].

X

Y Z

(b )

Figure 1.A target moving through a ?eld of three binary proximity sensors,X ,Y and Z ;(b )shows sensor outputs as a function of time;(c )shows the localization patches to which the target is localized over time intervals with constant signature;and (d )shows the arcs marking boundaries between patches.Particle ?lters [5]offer an alternative to Kalman ?lters in non-Gaussian setting,and have been investigated for track-ing using sensor networks in [4].Most prior work on particle ?ltering assumes more sensed information (with a more de-tailed probabilistic model)than provided by the binary sens-ing model of this paper.Khan et al.[10,11]have used par-ticle ?ltering for an insect tracking application,where the insect targets are assumed to interact according to a Markov random ?eld.Fox et al.[7]provide a survey of particle-?lter based methods for the problem of mobile robot localization,where robots wish to determine their location,and the lo-cations of other robots,using sensory inputs.Our contribu-tion in this paper is to provide a particularly simple parti-cle ?ltering algorithm that provides robust performance us-ing the minimal information obtained from non-ideal binary sensors.

2The Geometry of Binary Sensing

In this section,we describe an idealized model for a bi-nary sensor network,and the structure of the geometric in-formation it provides regarding a target’s location.This ge-ometric structure forms the basis for our theoretical bounds and algorithms.

Consider a network of n sensors in a two-dimensional plane.Each sensor detects an object within its sensing re-gion,and generates one bit of information (1for presence and 0for absence)about the target;we call this the ideal binary sensing model .We get no other information about the location,speed,or other attributes of the target.The in-formation of a sensor is ef?ciently encoded by the transitions between its 0and 1bits,and so its output can be summarized by the timestamps marking these transitions.We assume that the sensors are synchronized in time to suf?cient accuracy—several timing synchronization algorithms are available in the literature (e.g.,[6]).We also assume that the location of each sensor is known—the sensor locations can be recorded at the time of deployment,or can be estimated using local-ization techniques (e.g.,[18]).

Our emphasis is on discovering and attaining fundamen-tal limits of tracking performance,and therefore we abstract away lower layer networking issues by assuming that the sensor observations are communicated to,and fused at,a

tracker node.Given the minimal communication needs of bi-nary proximity sensors,such a centralized architecture may well be the most attractive choice for implementation in many settings,using multihop wireless communication be-tween the sensors and the tracker node(s).In any case,it is relatively straightforward to develop communication-and storage-ef?cient hierarchical distributed versions of our cen-tralized algorithms:for example,the tracker node can be chosen dynamically based on the target’s location,and it can convey its summary of the particular segment of the target’s trajectory to the next tier of the hierarchy.

For simplicity,we assume that each sensor has a circular sensing region of radius R :a sensor outputs a 1if a target falls within the sensing disk of radius R centered at its loca-tion.The parameter R is termed the sensing range.However,our framework also applies to sensing regions of more com-plex shapes that could vary across sensors.We assume noise-less sensing for the time being:the sensor output is always 1if a target is within its sensing range,and always 0if there is no target within its sensing range,with 100%accuracy.Methods for handling noisy sensor readings are considered in later sections.

The geometry of binary sensing is best illustrated via an example.Figure 1(a)shows a target moving through an area covered by three sensors.Figure 1(b)shows the sensor out-puts as a function of time.We de?ne the signature of any point p in two-dimensional space as the n -bit vector of sen-sor readings,whose i th position represents the binary output of sensor i for a target at location p .2In Figure 1,if we de?ne the signature as the bits output by sensors X ,Y and Z in that order,then the target’s signature evolves over time as follows:000,100,110,010,011,001,000.Initially,it is outside the sensing disks of all three sensors;then it enters the disk of X ,then Y ,then it leaves the disk of X ,enters that of Z ,and so on.The time instants {t j }mark the transi-tions when the target either enters or leaves a sensor’s range.Figure 1(c)shows that the target can be localized within a lo-calization patch F j during the time interval [t j ,t j +1),which

2The

notion of signature is a conceptual tool.Our algorithms

do not actually use the entire bitmap for a given target location,but work with a much smaller localized version,as explained in the next section.

corresponds to the set of possible locations corresponding to the signature during this interval.When the target moves from a patch F j to the next patch F j+1,we note that exactly one sensor’s bit changes:either the target enters the sensing disk of some sensor,or it leaves the disk of some sensor.The two patches,F j and F j+1,therefore,share a localization arc A j of the disk of the sensor whose reading has?ipped,as shown in Figure1(d).A simple but important observation is that,at the transition times t j,the two-dimensional uncer-tainty in the target’s location is reduced to a one-dimensional uncertainty.

In general,a localization patch need not be connected and, correspondingly,the localization arc of two such patches can also have two or more pieces.(As a simple example,con-sider three sensing disks A,B,C,respectively,centered at points(0,0),(1,0),and(2,0),where the radius of the disks is1.5.Then,the patch with signature(0,1,0)has two dis-connected pieces—these are the regions that are inside disk B but outside A and C.)Although disconnected patches are mainly an artifact of low sensor density,one can also create pathological examples where a patch can have two pieces even under high density.The non-connectivity of patches, however,does not impact the tracking resolution,because our Theorem2ensures that even if a patch is disconnected, all of its pieces lie within the resolution bound of each other.

The preceding geometric information structure forms the basis for our results in subsequent sections.Our derivation of fundamental limits in Section3is based on estimation of the size of the regions F j.The geometric algorithms for comput-ing minimal description trajectory estimates consist of com-puting piecewise linear approximations that pass through the patches F j or the arcs A j in the order speci?ed by the evolu-tion of the target’s signature.

3Fundamental Limits

We assume ideal sensing with sensing range R for each sensor,and an average sensor density ofρsensors per unit area.Thus,the performance limits we derive depend only on the parametersρand R.We?rst show that the spatial resolution cannot be better than order of1ρR,regardless of the spatial distribution of the sensors.We then show that this resolution can be achieved using standard uniform random distributions as well as regular grids.Finally,we show that binary sensing is analogous to discrete sampling,in that it provides information only about a“lowpass”version of the target’s trajectory,and discuss the implications for obtaining minimal path representations and velocity estimates.

3.1An Upper Bound on Spatial Resolution

The localization error of an estimated trajectory is de-?ned to be the maximum deviation of the estimated path from the actual path.This is just the L∞norm of the differ-ence between the actual and estimated trajectories,viewed as functions of time.The spatial resolution of a binary sensor ?eld is the worst-case localization error for any target trajec-tory through the?eld.

As observed in Section2,binary sensing localizes a target to within a patch corresponding to a speci?c signature,or bit vector of sensor outputs.The spatial resolution is therefore given by the diameter of the largest patch induced by the binary sensing?eld.In the following,we argue that in any con?guration of sensors,this diameter is lower bounded by c

ρR

,for an absolute constant c,which gives an upper bound on the achievable resolution.

Figure2.Illustration for Theorem1.

T HEOREM 1.If a network of binary proximity sensors has average sensor densityρand each sensor has sensing radius R,then the worst-case L∞error in localizing the target is at leastΩ(1/ρR).

P ROOF.We are interested in asymptotic behavior and so we assume that the sensor?eld is large relative to R,and we can ignore the boundary behavior by focusing on the portion of the?eld that is at least R away from the boundary.Since the average sensor density isρin the?eld,there must be a circular region of radius2R that contains at most(the average number of)N=ρ(4πR2)sensors in it.Let x be the center of this circle,let C1denote the circle of radius R centered at x, and let C2be the circle of radius2R centered at x.(See Fig.2 for illustration.)We observe that only the sensors contained in C2can sense a target that lies in C1.Since there are at most N such sensors,their sensing disks can partition the inner circle C1into at most N2?N+2“patches”.On the other hand,the circle C1has areaπR2,so at least one of the patches must have area at least cπR2/N2,for some constant c.Plugging in the value of N,we get that some patch in C1 must have area at least

cπR2

16π2ρ2R4

=Ω(1

ρ2R2

).

Therefore,the diameter(the longest projection)of this patch is at leastΩ(1ρR),the square root of the area,which proves the claim.

Theorem1makes no assumptions on the distribution of sensors:it only makes use of the average sensor density bound,and upper bounds the best resolution one can hope to achieve in an ideal deployment.In the next subsection, we address the complementary question:is this ideal resolu-tion achievable,and what distributions of sensor nodes can realize this?Our investigation here is analytic,with a goal to show that certain simple con?gurations of sensors lead to regions where the maximum L∞error matches the bound of Theorem1.Algorithmic questions of computing compact trajectory approximations are addressed in the following sec-tion.

3.2Achievability of Spatial Resolution Bound

The spatial resolution of Theorem1can be achieved(ne-glecting edge effects)by simply arranging the sensors in a regular grid.Since such an ideal deployment is often impos-sible in practice,we now show that a random Poisson distri-bution with densityρalso achieves the desired resolution.In the process,we also derive a sharp tail bound on the size of a localization patch.

Mathematically,the Poisson distribution of meanρmeans that(i)the number of sensors in a region of area A is a Poisson random variable N A with meanρA,and(ii)for two nonoverlapping regions,the corresponding numbers of sensors are independent random variables.We assume an asymptotic regime in which the probability of a point in the plane being within range of at least one sensor tends to one. For an arbitrary point P,this condition is satis?ed if there is at least one sensor in a disk of radius R centered at P. Thus,P[no sensor in disk of radius R]=e?ρπR2→0which requires thatρR2→∞.(In practice,values ofρR2of the order of4or more suf?ce to guarantee adequate coverage). The following theorem states our result.

Figure3.Illustration for proof of Theorem2.

T HEOREM 2.Consider a network of binary proximity sen-sors,distributed according to the Poisson distribution of den-sityρ,where each sensor has sensing radius R.Then the localization error at any point in the plane is of order1ρR.

P ROOF.See Figure3for an illustration.Consider an arbi-trarily chosen point P in the plane,and an arbitrarily chosen direction of movement,starting from that point.Given the isotropic nature of the Poisson distribution,without loss of generality,this direction can be chosen as going right along the horizontal direction.Let X denote the minimum move-ment required in that direction before there is a change in signature(i.e.,before the boundary of some sensor’s disk is crossed).We wish to characterize the tail of the distribution of X.

To this end,consider a point Q that is a distance x away from P along the direction of movement,as shown in Fig-ure3.Any sensor detecting P(resp.Q)must lie in the disk of radius R with center at P(resp.Q).Thus,P and Q have the same signature if and only if the symmetric difference of these two disks(the shaded region in Figure3)contains no sensor,assuming that either P or Q is detected by at least one sensor.(Under the assumption thatρR2is large,the last condition is met with high probability.)Letting A d and A u,respectively,denote the area of the symmetric difference and the union of the two disks,it follows from the Poisson distri-bution that

P[X>x]=e?ρA d

1?e?ρ(A u?A d)

1?e?ρA u

,0≤x≤2R

(We note that A d≤A u,with equality for x≥2R,so that P[X>x]=0for x≥2R.Thus,X is upper-bounded by2R.) Elementary geometric calculations yield that

A d=(2γ+sin2γ)R2

whereγis the angle shown in Figure3,satisfying sinγ=x

2R

. For our purpose,it suf?ces to loosely bound A d below as

A d≥2R2sinγ=xR

(usingγ≥sinγ).This implies that

P[X>x]≤e?ρA d≤e?ρRx,(1) which guarantees the promised asymptotic decay with c,for x=cρR.In fact,the exponent of decay is approximately twice as large as that used in our proof:this follows because the values of x,andγ,we are considering are small,and A d~2xR,which yields P[X>x]~e?2ρRx.

3.3Remarks on Spatial Resolution Theorems

Theorems1and2show that the spatial resolution cannot be better than O(1ρR),and that this resolution can be achieved with a random(Poisson)sensor deployment.The depen-dence on sensor density seems to match common intuition: the more sensors we have,the better the spatial accuracy one should be able to achieve.On the other hand,the dependence on sensing radius may seem counterintuitive—because these are binary proximity sensors,they do not actually measure the distance to the target,and so having a large sensing radius may seem like a disadvantage.Indeed,as the sensing radius increases,we seem to get less information from an individual sensor:its1bit localizes the target to a larger area.Neverthe-less,as our theorem shows,at the system level,the accuracy improves with larger sensing radius.This is a good example of the advantage of networked sensing,where the increase in an individual sensor’s uncertainty is counter-balanced by a quadratic increase in the number of patches into which the sensor?eld is partitioned by the sensing disks.When the sensing radius is small,the sensing disks are either disjoint or overlap only a little,and there are only O(n)patches.As the radius begins to grow,more disks pairwise intersect,and at suf?ciently large radius,all pairs intersect,partitioning the sensor?eld intoΘ(n2)patches,thereby reducing the size of each patch and improving the localization accuracy.In a?-nite sensor?eld,of course,this improvement stops when the radius becomes comparable to the length of the?eld.

Our theorems also help explain some of the empirical re-sults of Kim et al.[12]for target tracking using binary prox-imity sensors.They found that for a?xedρR2(which we can interpret as?xing the average number of sensors that can detect a target at a given position),better accuracy was achieved for the combination of“higher density and smaller

radius”than “lower density and larger radius,”leading them to propose that deployments with higher sensor density and smaller sensing radius are preferable.This empirical obser-vation is a direct consequence of our theoretical results:for constant ρR 2,reducing the sensing radius by 1/2corresponds to a factor of 4increase in the density,while reducing the density by 1/2corresponds to √2increase in the radius.The former combination yields a higher value of ρR ,which im-plies better spatial resolution.

Finally,our resolution theorems easily generalize to any ?xed dimension,and we can show that the achievable reso-lution in d dimensions is Θ(1/(ρR d ?1)).

3.4Sampling and Velocity Estimation

The geometric information structure introduced in Sec-tion 2shows that binary sensors can only localize the target to localization patches,and the resolution theorems of Sec-tion 3show that these patches attain localization accuracy of Δ=O (1ρR ).Thus,as far as spatial accuracy is concerned,nothing further remains to be said.For any sequence of patch boundaries crossed by the target,there are in?nitely many candidate trajectories crossing those patches in the same or-der,and any one of which is as good as another because they all lie within the achievable localization accuracy.Clearly,however,all these paths are not equally attractive as an es-timate of trajectory.On grounds of “representational fru-gality,”perhaps one would prefer a path that uses a small number of segments as opposed to the one that uses a large number of segments.A different criterion may be to choose paths that track the second important quantity of interest in target tracking:its velocity.It turns out that these two top-ics (path representation and velocity estimation)are in fact closely related,and are the focus of this section.

Figure 4.A trajectory exhibiting high frequency varia-tions that cannot be captured by binary sensors.Our starting point is an analogy between binary sens-ing and analog-to-digital conversion based on sampling and quantization,which immediately suggests that only a “low-pass”version of the trajectory can be reproduced.Consider,for instance,the trajectory shown in Fig.4,which corre-sponds to the same sensor outputs as the trajectory of Fig.1but includes “high-frequency”variations around a slowly varying trend.Within the spatial resolution afforded by our sensor model,these two trajectories are indistinguishable.The high-frequency trajectory of Fig.4,however,clearly has a higher velocity than the smooth trajectory of Figure 1.But,as we note below,the high-frequency component of its

velocity cannot be estimated based on binary sensor read-ings.This suggests that,among many spatially equivalent paths,piecewise linear approximations adequately represent the output of the sensor ?eld in terms of both spatial resolu-tion and velocity estimation.An analysis of velocity estima-tion errors using such piecewise linear representations leads to the intuitively pleasing conclusion that paths that use few segments (frugal representation)are also the paths that lead to good velocity estimation!These ideas lay the foundation for our algorithms (described in Section 4)that employ an Occam’s razor approach to the construction of estimated tra-jectories.

3.4.1Lowpass Trajectories

We begin with a simple but important interpretation of a binary sensor ?eld as a device for spatial sampling.Let x (t )denote the two-dimensional vector specifying the true loca-tion of the target at time t .Using the notation of Section 2,we can say that x (t j )∈A j ,where {t j }are the times at which the target’s signature changes,and A j is the arc de?ning the boundary between the patches F j and F j +1.For a moment,assume that a genie or an oracle actually tells us the precise locations x (t j ),for the set of time instants {t j }.We can now infer the following about the velocity vector v (t )=d x /dt :

t j +1

t j

v (t )dt =x (t j +1)?x (t j ).

In other words,even with the genie’s aid,all that we can say about the target’s trajectory during the interval [t j ,t j +1)is that (i)the target is con?ned to the patch F j ,and (ii)the average vector velocity of the target in the patch is

v j =

x (t j +1)?x (t j )

t j +1?t j

We denote the corresponding scalar average velocity by v j =|| v j ||.

Note that we cannot infer anything about the deviation v (t )? v j in the vector velocity from its average over the path,since this deviation integrates to zero in the time interval [t j ,t j +1).This means that any high-frequency ?uctuations in the path that are of small enough amplitude to stay within the patch F j are entirely “invisible”to the binary sensor ?eld.Indeed,for a one-dimensional ?eld of sensors,the sam-pling and quantization interpretation is immediate,without requiring invocation of a genie:the patches reduce to inter-vals and the arcs reduce to points.In this case,the binary sensor ?eld is identical to a level-crossing analog-to-digital converter [19].

Therefore,at best we can hope to reconstruct a lowpass representation of the target’s trajectory,which we de?ne as a piecewise linear approximation over spatial scale Δ,with line segments connecting the sequence of points x (t 1),x (t 2),....Other de?nitions that interpolate more smoothly across the arcs A j are also possible,but the piecewise linear form has the virtue of being a minimal representation of the informa-tion obtained from the binary sensors and the genie (in par-ticular,it preserves information in the average velocity se-quence { v j }).

The trajectory shape and the velocity estimates for the

lowpass representation serve as a benchmark for comparing the output of any algorithm based on the sensor readings.

Since this benchmark is de?ned with the genie’s help(which

eliminates the spatial uncertainty at each arc A j),it is not attainable in practice without some additional assumptions

regarding the trajectory,as discussed in the next section. 3.4.2Velocity Estimation Error

The set of all piecewise linear paths that visit the sequence

of arcs A j in the order given by the sensor signature sequence forms an equivalence class under the spatial resolution:all

these paths are equivalent to the lowpass trajectory de?ned

by the genie within the spatial resolutionΔ.Let us call this set REP,for spatial Resolution Equivalence Paths class.Even

considering the lowpass representation,where all?uctua-

tions of spatial scale smaller thanΔare removed,two paths in REP can differ in length by a factor of2:in a triangle

of side lengthΔ,there are two possible paths,one of length Δthat follows one side,and one of length2Δfollowing the other two sides.More generally,one path can be a straight

line,and the other can zig-zag taking2Δlong detours for

each segment of lengthΔcovered along the straight line.

In the absence of any other information,we simply have

no way to decide which among the many candidate paths in

the equivalence class REP offers the best approximation to the true path.The only way to decrease this uncertainty is to assume additional conditions that help shrink the spread of the path lengths in the equivalence class.In the following,we identify simple and natural technical conditions under which all the paths in the equivalence class have roughly the same length,and therefore any choice is guaranteed to give a good approximation.In particular,just as the accuracy of spatial resolution is controlled by size of the localization patches, the accuracy of the velocity estimation is controlled by the variance in the path lengths of the equivalence class.

We consider minimal representations of the trajectory in

terms of piecewise linear approximations with line segments

spanning several patches,and ask when velocity estimations computed using such a representation are accurate.That is, we seek conditions under which the entire class of equivalent paths provides a good approximation to the genie-aided av-erage scalar velocity function,which is a piecewise constant sequence taking value v j over the time interval[t j,t j+1).

We?rst relate the relative error in velocity estimation to the relative spread in path lengths in the equivalence class REP.Suppose that the estimated trajectory is of length L between arcs A k and A k+m.Assuming that the scalar velocity is constant over this path segment,it can be estimated as the length divided by the time to go between arcs A k and A k+m: v=L/(t k+m?t k).Suppose the true trajectory between A k and A k+m has length L+δL.Then,our velocity estimate error isδv=δL/(t k+m?t k).We therefore obtain that

δv v =

δL

L

(2)

That is,by considering the relative variationδv

v in velocity

rather than the absolute variationδv,we are able to remove dependence on time scaling.The results we derive depend,therefore,only on the spatial variations of the path shape and its velocity,and not on time scale.

The main consequence of Eq.(2)is that,if L is large enough and the permissible variationδL(constrained both by the sensor readings and the assumptions we make about the true trajectory)is small enough,then we can obtain accu-rate velocity estimates.For example,in order for a velocity estimate to be accurate to within10%,we need to be able to guarantee thatδL≤0.1L.If we assume that the scalar velocity is constant over large enough path sections,then we may be able to accurately estimate the velocity as long as the variations in path lengths consistent with the sensor readings can be controlled.Controlling the path length?uctuations is the same as bounding the path length spread in our equiva-lence class REP.

The following theorem characterizes the intrinsic ambigu-ity(caused by the spatial resolutionΔ)in velocity estimation based on straight line approximations,arguing the the rela-tive spread in path lengths is small if the line segment is long enough.

T HEOREM 3.Suppose a portion of the trajectory is approx-imated by a straight line segment of length L to within spatial resolutionΔ.Then,the maximum variation in the velocity es-timate due to the choice of different candidate straight line approximations is at most

δv

v

≤2

Δ

L

2

.

Furthermore,this also bounds the relative velocity error if the true trajectory is well approximated as a straight line over the segment under consideration.

P ROOF.By our spatial resolution theorem,the true trajec-tory is(approximately)a straight line that must lie within Δof the straight line approximation s we are considering. That is,it must lie in a rectangle of width2Δwith s as its long axis.The maximum deviation in length from the ap-proximation s is if the true trajectory is the diagonal of this rectangle,whose length is L+δL=2

(L/2)2+Δ2,which

yieldsδL≈2Δ2

L

forΔ L.Clearly,this bound also applies for deviation of the true trajectory from the straight line ap-proximation being considered,as long as the true trajectory is well approximated as a straight line over the current seg-ment.We now apply Eq.(2)to obtain the desired result.

Theorem3implies that,if we want to control the relative velocity error to less thanεusing a piecewise linear approxi-mation,then the length of each line segment must be at least

L≥L0=

ε

.(3)

As an example,to achieve error at most10%,segments of length5Δsuf?ce;error of5%requires segments of length ≈6.32Δ.Put another way,if on average each linear approx-imation segment spansαlocalization arcs,then the average relative velocity error is dv/v≤2/α2.Our simulation re-sults show that even for fairly complex(synthetic)trajecto-ries,a piecewise linear approximation works well,withαat least10on average.

Note that,for trajectories that“wiggle”while staying withinΔof a(long enough)straight line,Theorem3can be interpreted as guaranteeing accuracy in estimation of the projection of the velocity along the straight line.On the other hand,if a trajectory curves sharply,piecewise linear approx-imations to withinΔof the trajectory must necessarily use shorter line segments,making the velocity estimation error worse.But this is unavoidable because over short spans,the relative difference between two linear segments is larger,as implied by Theorem3:in the extreme case,where L≈Δ,we are back to the factor of two error discussed at the beginning of Section3.4.2.

4Tracking Algorithms

The theoretical considerations of the previous section mo-tivate an Occam’s razor approach to tracking.Among all the candidate paths meeting the spatial resolution bound,a piecewise linear approximation that uses a minimal number of segments has the advantage of compact representation as well as accurate velocity https://www.360docs.net/doc/251745843.html,ing the notation of Section2,we know that the target is constrained to lie in re-gion F j during the time interval[t j,t j+1],where{t j}are the time instants at which there are changes in the bit vector of sensor outputs.We formally de?ne a localization region F j, corresponding to an interval[t j,t j+1),as follows,dropping

the subscript j for convenience.Let I be the subset of sen-sors whose binary output is1during the relevant interval, and let Z be the remaining sensors whose binary output is0 during this interval.Then the region F of the plane to which the target can be localized during this interval is given as:

F=

i∈I D i?

i∈Z

D i,

where D i is the sensing disk of radius R centered at sensor i.Note that it is not necessary to consider the entire set Z in order to determine F:it suf?ces to consider only those sensors whose disks can intersect with any disk in I.Thus,in our implementation,it is necessary only to maintain,for each sensor s,a neighbor list of all other sensors whose sensing disks intersect with the disk of s

.

Figure5.The shaded band shows the regions{F i}to which the trajectory is localized by the sensor outputs.

Fig.5shows an example trajectory and the band consist-ing of the regions.Any trajectory that traverses these regions in the order speci?ed by the sensing outputs is consistent with the target’s true trajectory,within the accuracy bounds of the model.Among all these possible trajectories,the Oc-cam’s razor approach prefers the one that is the simplest.For instance,if all the regions could be traversed by a single line, then a linear trajectory has the simplest descriptive complex-ity,within the theoretical accuracy of tracking.Generalizing this,a piecewise linear trajectory with the fewest number of linear segments that traverses all the sensed regions in order is the trajectory of minimal complexity.In the following sec-tion,we describe a geometric algorithm,O CCAM T RACK,for computing such a trajectory.Our computational model as-sumes that a tracker node collects the output from the sensor nodes,and runs the algorithm to compute the trajectory.The algorithm,however,can also be implemented in a distributed fashion by exchanging data among the neighboring nodes.

4.1The O CCAM T RACK Algorithm

Algorithm1below describes the O CCAM T RACK at a pseudo-code level.S is the set of all the sensors,and the algo-rithm operates in discrete time steps T,which are simply the instants at which one of the sensor’s binary state changes.At each of these discrete time steps t,the algorithm determines the sets I and Z,and computes the region F localizing the target.The time-ordered sequence of these regions F is the spatial band B that contains the target’s trajectory.The func-tion M IN S EG P ATH then computes a minimum piecewise lin-ear path traversing the band.

Algorithm1O CCAM T RACK(S)

1:T←{s.start,s.end:?s∈S};

2:sort(T);

3:for all t∈T do

4:I←{s:t∈[s.start,s.end]};

5:Z←{s:s∈I.nbrlist∧t/∈[s.start,s.end]};

6:F←

i∈I

D i?

i∈Z

D i;

7:B←B∪F;

8:end for

9:L←M IN S EG P ATH(B)

;

Figure6.The path computed by O CCAM T RACK has3 line segments.The sequence of arcs delineating the re-gions of band B are shown in thick lines.

In the pseudo-code for M IN S EG P ATH,the function F IND-

A RCS determines the ordered sequence of localization arcs corresponding to the localization band B.3The function

F IND L INE either determines that a subsequence of arcs

q i,q i+1,...,q j cannot be“stabbed”(in the given order)by a single line,or?nds such a stabbing line.The algorithm

M IN S EG P ATH uses this function in a greedy fashion to?nd

the longest pre?x of arcs that can be approximated by a sin-gle line segment,removes those arcs,and then iterates on the remaining sequence.There are only a?nite number of combinatorially distinct candidate lines one needs to test to decide if a sequence of arcs can be stabbed by a line.In particular,it suf?ces to test the lines formed by pairs of end-points of arcs,or lines that are tangent to some arc.4Figure6 shows the minimal description path for the example of Fig-ure5.

Algorithm2M IN S EG P ATH(B)

1:A←F IND A RCS(B);

2:i←1;

3:for all j∈1,2,...,m do

4:if?F IND L INE(A i,A i+1,...,A j)then

5:L←L∪F IND L INE(A i,A i+1,...,A j?1);

6:i←j;

7:end if

8:end for

4.2Analysis of O CCAM T RACK

By construction,the piecewise linear path computed by

O CCAM T RACK intersects the regions of the band B in the same order as given by the binary sensors outputs—this fol-lows because M IN S EG P ATH constrains the path to visit the boundary arcs of consecutive regions in order.This,how-ever,does not mean that the true trajectory and the piecewise linear path visit the same sequence of regions.The linear shortcuts found by O CCAM T RACK can visit additional re-gions.This can happen if the linear segment crosses over a non-convex vertex of one of the regions.The important point to note,however,is that the maximum distance be-tween the true trajectory and the computed path at any instant (the L∞error)is still bounded byΔ=O(1/ρR),because the path computed by O CCAM T RACK does lie entirely within the union of the convex hulls of the F regions in the band B.Since the diameter of the convex hull of any F region is bounded byΔ,the error guarantee follows.The following 3While a localization arc can have multiple pieces in a patholog-ical case,the union of all its sub-arcs is still within the resolution bound(cf.Theorem2).Thus,for the purpose of minimal path rep-resentation,we can safely“interpolate”all the disconnected pieces of the arc and still remain within the tolerable error.However,to keep our implementation simple,we chose to ignore such patho-logical contingencies,and opted to simply ignore an arc if it were found to be disconnected.

4In computational geometry,several theoretically more ef?cient methods(e.g.[8])are known for these stabbing problems,but they are complicated to implement and involve signi?cant overhead in data structures.We chose to implement our algorithm because it is simple,compact,works fast in practice.theorem shows that the worst-case path approximation com-

puted by M IN S EG P ATH uses at most twice the number of optimal segments.(In practice,it is very close to optimal.)

Due to lack of space,we omit the proof of this theorem.

T HEOREM 4.The algorithm O CCAM T RACK computes a

piecewise linear path that visits the localization arcs in or-der and uses at most twice the optimal number of segments in the worst-case.If there are m arcs in the sequence,then the worst-case time complexity of O CCAM T RACK is O(m3).

4.3Robust tracking with non-ideal sensors

Figure7.The non-ideal sensing model The O CCAM T RACK algorithm assumes ideal binary sens-

ing.In practice,sensing is imperfect and noisy:a sensor could detect an object outside its nominal range,or it may

fail to detect an object inside its range.We illustrate our ap-

proach to such non-idealities using a sensing model in which the target is always detected within an inner disk of radius

R i,called the detection region,and is detected with some

nonzero probability in an annulus between the inner disk and an outer disk of radius R o,called uncertain region.Targets

outside the outer disk are never detected.Figure7gives an il-

lustration of this model.Despite its simplicity,such a model is of fairly broad applicability,since it arises naturally if sen-

sors integrate noisy samples over a reasonable time scale to

make binary decisions regarding target presence or absence.

The main implication of the model in Figure7for the

O CCAM T RACK algorithm is that we can no longer identify

circular arcs corresponding to an object entering and leaving

a sensor’s detection range.While we can employ O CCAM-T RACK algorithm directly by approximating the sensing re-

gion as a disk of some radius R,where R i≤R≤R o,sim-ulations show that the performance can be poor.We there-fore consider an alternative approach,in which we employ

a particle?ltering algorithm to handle non-idealities.While

this produces a good approximation of the true trajectory, it is not amenable to an economical description.We there-fore employ a geometric post-processing algorithm to obtain a minimal representation for the output of the particle?lter-ing algorithm.While particle?ltering is a well established technique,the main novelty of the algorithm presented here is the way in which it exploits the constraints of the sensing model for a simple and ef?cient implementation.

In order to illustrate robustness to non-ideal sensing,we

take a worst-case approach to the information provided by the non-ideal sensing model in Figure7,assuming the max-imal uncertainty consistent with the sensor readings.If a

sensor output is1,then we assume that the target is some-where inside the large disk of radius R o centered at the sen-sor.If a sensor output is0,then we assume that the target is somewhere outside the small disk of radius R i centered at the sensor.A localization patch F at any time instant is given by intersecting all such areas,just as before.

4.3.1Particle Filtering Algorithm

We now sketch the particle?ltering algorithm;a more de-tailed description and software implementation is available from the authors upon request.At any time n,we have K particles(or candidate trajectories),with the current location for the k th particle denoted by x k[n].At the next time instant n+1,suppose that the localization patch is F.Choose m can-didates for x k[n+1]uniformly at random from F.We now

have mK candidate trajectories.Pick the K particles with the best cost functions to get the set{x k[n+1],k=1,...,K}, where the cost function is to speci?ed shortly.Repeat un-til the end of the time interval of interest.The?nal output is simply the particle(trajectory)with the best cost function. Thus,Monte Carlo simulation is an intrinsic part of this algo-rithm,since random sampling is employed to generate candi-dates for evaluation.The sampling time interval is chosen to be short compared to a localization patch,so as to generate a suf?ciently rich set of candidates.

It remains to specify the cost function.We chose an addi-tive cost function that penalizes changes in the vector veloc-ity,in keeping with our restriction to lowpass trajectories. Once a candidate x k[n+1]is chosen from the current lo-calization patch,the increment in position x k[n+1]?x k[n] is an instantaneous estimate of the velocity vector at time n.The corresponding increment in the cost function is the norm squared of the difference between the velocity vector estimates at time n and n?1.This is given by

c k[n]=||(x k[n+1]?x k[n])?(x k[n]?x k[n?1])||2

=||x k[n+1]+x k[n?1]?2x k[n]||2

The net cost function for a candidate trajectory up to time n is simply the sum of these incremental costs:∑n m=1c k[n]. 4.3.2Geometric Postprocessing

The particle?ltering algorithm described above gives a robust estimate of the trajectory consistent with the sensor observations,but it provides no guarantees of a“clean”or minimal description.This suggests the possibility of apply-ing the geometric approach of Section4.1to the particle?lter estimate to generate a more economical description.We omit details due to lack of space,but provide a brief pseudo-code description of an algorithm F IT L INE to generate a piecewise linear approximation with a small number of line segments. In the pseudo-code,p is the ordered list of samples generated by the particle?ltering algorithm,L is the output piecewise linear approximation,and function L INE S EGMENT(Q)re-turns a line segment that is within distanceΔof the sequence of points Q.Algorithm3F IT L INE(p)

1:Q←φ;

2:for all i∈1,2,...,|p|do

3:if E RROR(Q∪p i)>Δthen

4:L←L∪L INE S EGMENT(Q);

5:Q←φ;

6:end if

7:Q←Q∪p i;

8:end for

5Simulation Results

We carried out extensive simulation tests to evaluate the performance of all our algorithms,under both ideal and non-ideal sensing models.The code for O CCAM T RACK was written in C and C++,the code for P ARTICLE-F ILTER was written in Matlab,and the experiments were performed on an AMD Athlon1.8GHz PC with350MB RAM.We?rst discuss our results for the ideal sensing model.

5.1O CCAM T RACK with ideal sensing

Our general experimental setup simulated a1000×1000 unit?eld,containing900sensors in a regular30×30grid. The sensing range for each sensor was set to100units.When evaluating the scaling affects of the sensor parameters,we kept the?eld size and one parameter?xed,while the other parameter(radius or density)was varied.

We used geometric random walks to generate a variety of trajectories.Each walk consists of10to50steps,where each step chooses a random direction and walks in that direction for some length,before making the next turn.Each trajectory has the same total length,and we generated50such trajecto-ries randomly.

5.1.1Quality of trajectory approximation

On all50random walk trajectories,O CCAM T RACK de-livers excellent performance.Figure8(b)is a typical exam-ple,where the true trajectory is virtually indistinguishable from the approximation computed by O CCAM T RACK.

We also ran the weighted-centroid algorithm of Kim et al.[12]on these trajectories.In our comparison,we used the adaptive path-based version of their algorithm,which is claimed to be well-suited for complex and non-linear trajec-tories.For ease of reference,however,we still refer to this algorithm as the weighted-centroid scheme.We ran this al-gorithm with inner and outer radii both equal to the ideal radius100.

Figure8(a)shows the output of the weighted-centroid method,and is typical of its performance on all our ran-dom walk trajectories.The weighted-centroid algorithm is sample based,and it used1000vertices to approximate each of the trajectories.By contrast,the O CCAM T RACK used between20and70vertices.Despite this frugal represen-tation,the maximum localization error for O CCAM T RACK was always smaller than the weighted-centroid,on average by30%,and in some cases by a factor of?ve.Due to its highly ef?cient structure,O CCAM T RACK is also300times faster than weighted-centroid.In all cases,our algorithm

(a )Weighted-Centroid (b )O CCAM T RACK (c )Velocity estimate by O CCAM T RACK

Figure 8.Quality of trajectories produced by the weighted-centroid algorithm of [12]and O CCAM T RACK .Figure (c)shows the results of velocity estimation by O CCAM T RACK .took less than 10milliseconds,while weighted-centroid took between 2and 20seconds.

5.1.2Velocity estimation performance

In our random walk trajectories,we also varied the scalar velocity randomly at each turn,and then used O CCAM -T RACK to estimate the scalar velocity along the trajectory.For each linear segment in the piecewise linear path com-puted by O CCAM T RACK ,we used the ?rst and the last lo-calization arc to determine the time spent on that segment;recall that sensor outputs tell us the exact times for each arc.We estimate the scalar velocity for this segment by dividing the length of the segment by this time.With a goal of es-timating the velocity within ε=0.1,namely,10%,we esti-mated the average velocity only over path segments of length at least L =√2Δ/√

ε,as given by Eq.3.

In Figure 8(c),we show the results of estimating the ve-locity for the sample trajectory of (b).The top ?gure shows the overlay of both the true and the estimated velocities along the trajectory,and one can see that the two agree very well.In the bottom ?gure,we plot the relative error in the velocity to highlight deviation.The ?gure shows that the maximum deviation is always less than 10%,as predicted by theory.The results were very similar for all 50trajectories.In particular,on average a segment of O CCAM T RACK ’s trajec-tory spanned about 15patches,meaning that an average line segment in the approximation has length L =15Δ,meaning that the velocity estimates are good,as explained by Theo-rem 3.

In the following two experiments,we evaluated the lo-calization accuracy of O CCAM T RACK with varying ρand R over many random trajectories,to see how it compares to the theoretical predictions of our theorems.

5.1.3Spatial resolution as a function of density

In this experiment,we measured the maximum error in localizing the target’s trajectory for a varying values of the sensor density.We kept the size of the ?eld and the sensing radius R ?xed,and then varied the number of sensors in the ?eld from n =100to n =6400.(Since the area of the ?eld is 106,this corresponds to variation in density from 10?4to 6.4×10?3.)We tried both the regular grid arrangement of the sensors,as well as the random placement.

10 20 30 40 50 60 0 1000 2000 3000 4000 5000 6000 7000

M a x E r r o r i n L o c a t i o n

Density (rho)

Grid Random

Theoretic curve: 1/rho

Figure 9.Spatial resolution vs.sensor density.By the spatial resolution theorem,the localization error should decrease inversely with the density.Figure 9shows that the measured error follows closely the theoretical curve of 1/ρ,both for the grid as well as the random placement.In each case,the reported error is the maximum error for the trajectory,averaged over 50random walk trajectories.(The average error for each trajectory is much smaller.)

5.1.4Spatial resolution as a function of sensing

range

In this experiment,we kept the density constant at 900nodes in the ?eld,and varied the sensing radius from 50to 400units.Figure 10shows the maximum error,averaged over 50random walk trajectories,for various values of the sensing range.By the spatial resolution theorem,the lo-calization error should decrease inversely with the sensing range,and again the measured values closely follow the the-oretical curve of 1/R .

5 10 15 20 25 30 50 100 150 200 250 300 350 400 450 500

M a x E r r o r i n L o c a t i o n

Sensing Radius (R)

Grid Random

Theoretic curve: 1/R

Figure 10.Spatial resolution vs.sensing radius.

(a)O CCAM T RACK(b)Particle Filter(c)Particle Filter+Geometric

Figure11.Trajectories computed by the three algorithms under the non-ideal sensing model.

5.2Tracking with Non-Ideal Sensing

We now describe the results of our experiments with non-

ideal sensors.Our model of non-ideal sensors is the one

shown in Figure7,where in the region between distance R i

and R o,the target is detected with probability1

2.An imper-

fect detection is problematic for the ideal geometric algo-rithm O CCAM T RACK because it relies on contiguous time intervals during which the target is inside the range.We used a simple hysteresis process to mitigate the affect of erratic detection:to signal the beginning of a detection interval,we require the sensor to output a1bit for3consecutive time samples;similarly,to signal the end of a detection interval, we require the sensor to output a0bit for3consecutive time samples.

We generated a variety of geometric trajectories,sim-ulated the sensor outputs using our non-ideal sensing model,and ran O CCAM T RACK,P ARTICLE-F ILTER,and P ARTICLE-F ILTER with geometric post-processing,which we call P ARTICLE F ILTER+G EOMETRIC.A sample trajec-tory,along with the outputs of the three algorithms,is shown in Figure11.

As expected,the ideal algorithm O CCAM T RACK per-forms poorly when the data is imperfect:such data lead to gaps in the sequence of localization patches and infea-sible localization arcs.In our implementation,we simply ignored these geometric inconsistencies,and just computed the piecewise linear paths using the rest of the arcs.Of course,in the worst-case,poor data can completely break O CCAM T RACK,but we found that the algorithm recovers rather well from these bad situations and produces accept-able trajectories,although not nearly as good as in the ideal case.In fact,compared to P ARTICLE-F ILTER,the output of O CCAM T RACK looks signi?cantly worse:it has sig-ni?cantly more pronounced turns and twists.P ARTICLE-F ILTER seems much better at dealing with noisy data,but its drawback is that,like any sample-based scheme,it produces trajectories with many vertices.This is where our combi-nation of P ARTICLE-F ILTER with geometric post-processing achieves the best of both worlds:it combines the robustness of P ARTICLE-F ILTER with the economic paths of the ideal O CCAM T RACK.

In particular,in the example of Figure11,the output of P ARTICLE-F ILTER+Geometric uses10segments and has maximum error of1.73,compared to51segments

required Figure12.The setup for our acoustic motes experiment.

0.2

0.4

0.6

0.8

1

0 20 40 60 80 100 120 140

P

r

o

b

a

b

i

l

i

t

y

Distance (cm)

Figure13.Probability of target detection with distance for an acoustic sensor.

for the P ARTICLE-F ILTER for the error of1.19.We sim-ulated this experiment over several trajectories,using the non-ideal sensing,and observed the same trend.On a typ-ical input,the maximum localization error using P ARTICLE-F ILTER+Geometric was comparable to the basic particle ?lter algorithm,but in the worst-case it was almost50% higher.On the other hand,the path description computed by P ARTICLE-F ILTER+Geometric was at least a factor of5 smaller.

6Mote Experiments

Finally,we set up a small lab-scale experiment using acoustic sensors to evaluate the performance of our algo-rithms.The setup consisted of16MICA2motes arranged in a4×4grid with30centimeter separation,as shown in Figure6.The motes were equipped with a MTS310sen-sor board,which has an acoustic sensor and a tone detec-tor circuit.(The tone detector can detect acoustic signals in a speci?c frequency range.)We adjusted the gain of the sound sensor so that the detection range for each sensor is

(a)O CCAM T RACK(ideal)(b)O CCAM T RACK(c)Particle Filter(d)Particle Filter+Geometric

Figure14.The output trajectories for the experiment using acoustic sensors.

about45cm.The target is also a MICA2equipped with

MIB310,which generated the acoustic signal using its on-board beeper.The target is then moved through the network

in a path(shown as the dotted trajectory in Fig.14).

We?rst performed some experiments with a stationary

target to determine the detection characteristics of the motes’

tone-detector.The readings from the motes turned out to be highly non-ideal.Not only did the motes make frequent

detection errors,but the probability of detecting a target was

not a monotonic function of the distance from the sensor,as shown in Fig.13.While this detection behavior is dif?cult

to model,it also means that this experiment is a good test for

the robustness of our tracking algorithms.

The results of our experiment are shown in Fig.14.The detection readings we collection from these experiments

showed a lot of non-ideal behavior.The most extreme be-

ing that one of the sensors,shown as double circle in the

?gure,failed to detect the target entirely,even though the target comes very close to it.

On the whole,however,even in presence of such extreme

failures,the results are very encouraging.All three algo-

rithms were able to give a reasonable estimate of the tar-get track.Figure14(a)shows the reference output for O C-CAM T RACK,assuming ideal sensing—that is,assuming the faulty sensor had also detected correctly,this is the trajec-tory O CCAM T RACK would produce.The other three?gures

show the outputs using the actual measurements from the

acoustic sensors.(The actual path of the target is shown as a dotted line,while the estimated trajectories are shown in bold lines.)As expected,the trajectory quality of O CCAM-T RACK suffers the most because of the failed sensor.The P ARTICLE-F ILTER does better,and again the combined al-gorithm preserves the robustness of P ARTICLE-F ILTER and approaches the minimal path description of O CCAM T RACK.

7Closing Remarks

We have identi?ed the fundamental limits of tracking per-formance possible with binary proximity sensors,and have provided algorithms that approach these limits.The results show that the binary proximity model,despite its minimal-ism,does indeed provide enough information to achieve re-spectable tracking accuracy,assuming that the product of the sensing radius and sensor density is large enough.Thus,this model can serve as a building block for broadly applicable and reusable tracking architectures.

The promising results obtained here and in[12],as well as the success of the large-scale deployment in[1],motivate a more intense investigation of tracking architectures based on the binary proximity model.In order to focus on fundamen-tals,we have considered a single path in our simulations and experiments.An in-depth understanding,and accompany-ing algorithms,for multiple targets is therefore an important topic for future investigation.We would also like to develop minimal modi?cations of the basic tracking architecture to incorporate additional information(e.g.,velocity,distance) if available.The particle?ltering framework appears to be a promising means for achieving this.In addition to exten-sions and implementation optimization of this framework, an interesting question is whether it is possible to embed Oc-cam’s razor criteria in the particle?ltering algorithm,rather than using geometric post-processing to obtain economical path descriptions.

Acknowledgment

We are grateful to the authors of[12]for giving us access to their target tracking code.

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区分对齐
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(通常会内缩) 斜体 加粗 引文(通常会以斜体显示) 码(显示原始码之用)

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3.0 大字 3.0 小字 与外观相关的标签(作者自订的表现方式) -------------------------------------------------------------------------------- 加粗 斜体 3.0 底线(尚有些浏览器不提供) 3.0 删除线 (尚有些浏览器不提供) 3.0 下标 3.0 上标 打字机体(用单空格字型显示)

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-

等标签进行定义的。

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This is a heading

This is a heading

定义最大的标题。

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B. < hr noshade> C. D. < td size=?> 9.以下表示段落标签的是( ) A. B. C.

D.
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12.以下表示声明框架标签的是( ) A. B. C. D. ,定义表格的行,用在
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html所有标签及其作用说明

html所有标签及其作用 ,表示该文件为HTML文件 ,包含文件的标题,使用的脚本,样式定义等 ---,包含文件的标题,标题出现在浏览器标题栏中 ,的结束标志 ,放置浏览器中显示信息的所有标志和属性,其中内容在浏览器中显示. ,的结束标志 ,的结束标志 其它主要标签,以下所有标志用在中: ,链接标志,"…"为链接的文件地址 ,显示图片标志,"…"为图片的地址
,换行标志

,分段标志 ,采用黑体字 ,采用斜体字


,水平画线
,定义表格,HTML中重要的标志

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表示绝对居中.
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B.
C.
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      定义有序列表,
        定义无序列表 23.定义列表的开始标记是
        24.定义表格的标记是标记,定义行的开始,,定义表格的行,用在
        定义行中的单元格 25.网页中设置滚动字幕的标记是标记 26.网页中版权声明符号标记是© 27.定义框架集的HTML标记是 28.框架集标记定义在标记之间。(F) 29.标记中用来定义滚动条的属性是scrolling 30.标记用来定义表单域 31.表单的提交方式有post和get 两种 32.关键字和描述信息定义在标记之间。 33. 标记定义页面标题,搜索引擎包括页面的信息,除标题以外的其他内容对访问者是不可见的。 34.标记必须用<> 括起来,标记一般成对出现。标记的属性带有特定的值,属性值包含在直引号中 35.网页文件标题标记 36.设置网页背景颜色通过在<body>标记中添加属性bgcolor实现 37.Xml语言Extensible Markup Language是指可扩展标记语言 38.XML是一种数据存储语言,XML标记用来描述文本的结构,而不是用于描述如何显示文本。 39.XML文件扩展名为”.xml” 40.XML文档主要由两部分组成:序言和文档元素,序言包括声明版本号、处理指令等。文档元素指出了文档 的逻辑结构,并且包含了文档的信息内容。一个典型的元素有起始标记、元素内容和结束标记。 41.CDATA节中的所有字符都会被当作元素中字符数据的常量部分,而不是XML标记。 42.将XML文档在浏览器中按特定的格式显示出来,需要css样式文件或者xsl样式文件告诉浏览器如何显示。 二、填空 1.body标记中Link属性定义未被访问过的链接颜色;alink属性定义链接激活状态的链接颜色;vlink属性定义 已被访问过链接的颜色 2.target属性用来定义链接打开窗口,当属性值为blank是定义链接在新窗口中打开。 3.<ol>定义有序列表,<ul>定义无序列表 4.定义表格的标记是<table>标记,<tr>定义行的开始,<td>定义行中的单元格 5.网页中设置滚动字幕的标记是<marquee>标记</p><h2>(完整版)初中音乐青春舞曲教案【三篇】</h2><p>初中音乐青春舞曲教案【三篇】 学唱歌曲《青春舞曲》,使学生能够把握歌曲的情绪、节奏,体会歌 曲的旋律特点。###小编整理了初中音乐青春舞曲教案【三篇】,希望 对你有协助! 青春舞曲教案一 一、教学理念: 根据新课程标准的指导思想,在音乐教学过程当中,把学生对音乐的 感受和参与放在重要的位置,充分利用现代化教学手段,提升课堂教 学效果,使学生在轻松愉快的学习气氛中体验美、感受美创造美,激 发学生的兴趣,提升学生的审美水平。在《青春舞曲》一课的教学中,主要是感受、体验新疆民歌的风格特点,让学生在学习过程中相互合作,充分发挥自主学习的水平、团结合作水平和创新水平。在表现歌 曲的多形式创作练习和参与音乐实践活动中,培养学生热爱民族音乐 的情感。 二、教学准备: 钢琴、CD碟片、小型打击乐器(手鼓、串铃等)、投影仪、音响设备等。 三、教学目标: 知识与技能:学会演唱歌曲,并能用活泼、有弹性的声音演唱《青春 舞曲》。准确把握歌曲的情绪,体会歌曲的旋律特点。 四、过程与方法: 1、尝试在聆听、模唱、讨论、创新中学习歌曲;通过音乐活动,调 动学生的积极参与,培养学生节奏感和创造力,训练协调性,加深对 歌曲风格的理解。</p><p>2、了解维吾尔族音乐特点,并可结合维吾尔族服装、乐器、舞蹈动作,体会音乐与舞蹈的结合。 五、情感态度与价值观: 通过学习维吾尔族歌曲《青春舞曲》及其相关知识,培养学生喜欢并 热爱我国的民族音乐,懂得青春易逝的道理,要珍惜大好时光,努力 学习。 六、教材分析: 这是一首G大调、4/4拍、单乐段的歌曲,短小精练,一气呵成,旋律活泼流畅,节奏具有鲜明的舞蹈性。感受体验维吾尔族民歌的风格特点, 七、教学重、难点: 1、重点:在听、唱、跳、等音乐活动中体验和表现歌曲的情绪。并 能用自然的声音准确地演唱《青春舞曲》。 2、难点:掌握维吾尔族民歌特点,能准确掌握歌曲节奏型,充分发 挥学生的创新水平及团结合作意识。并激发学生对“青春”的更深层 次的理解。 八、教学过程: (一)导入新课:1、以自己身上特有的民族特色来和学生实行讨论,抓住学生对少数民族的兴趣来导入新课。 (二)音画同步、提升兴趣 出示图片民族信仰,服饰,小吃,土特产,2、新疆的人们都能歌善舞,每逢喜庆、丰收时节,他们都用歌舞来表达自己的喜悦心情。另外新 疆这个民族有这独特的民族乐器(出示图片介绍新疆独特乐器)新疆 的音乐这么动听,新疆的舞蹈这么优美,让我们乘着去新疆的列车, 去学习一首新疆歌曲吧!</p><h2>监控系统安装流程(视频监控安装教程)</h2><p>监控安装指导与注意事项 A、线路安装与选材 1、电源线:要选“阻燃”电缆,皮结实,在省成本前提下,尽量用粗点的,以减少电源的衰减。 2、视频线:SYV75-3线传输在300米内,75-5线传输500米内,75-7的线可传输800米;超过500米距离,就要考虑采用“光缆”。另外,要注意“同轴电缆”的质量。 3、控制线:一般选用“带屏蔽”2*1.0的线缆,RVVP2*1.0。 4、穿线管:一般用“PVC管”即可,要“埋地、防爆”的工程,要选“镀锌”钢管。 B、控制设备安装 1、控制台与机柜:安装应平稳牢固,高度适当,便于操作维护。机柜架的背面、侧面,离墙距离,考虑到便于维修。 2、控制显示设备:安装应便于操作、牢靠,监视器应避免“外来光”直射,设备应有“通风散热”措施。 3、设置线槽线孔:机柜内所有线缆,依位置,设备电缆槽和进线孔,捆扎整齐、编号、标志。</p><p>4、设备散热通风:控制设备的工作环境,要在空调室内,并要清洁,设备间要留的空间,可加装风扇通风。 5、检测对地电压:监控室内,电源的火线、零线、地线,按照规范连接。检测量各设备“外壳”和“视频电缆”对地电压,电压越高,越易造成“摄像机”的损坏,避免“带电拔插”视频线。 C、摄像机的安装 1、监控安装高度:室内摄像机的安装高度以2.5~5米,为宜,室外以3.5~10米为宜;电梯内安装在其顶部。 2. 防雷绝缘:强电磁干扰下,摄像机安装,应与地绝缘;室外安装,要采取防雷措施。 3、选好BNC:BNC头非常关键,差的BNC头,会让你生不如死,一点都不夸张。 4、红外高度:红外线灯安装高度,不超过4米,上下俯角20度为佳,太高或太过,会使反射率低。 5、红外注意:红外灯避免直射光源、避免照射“全黑物、空旷处、水”等,容易吸收红外光,使红外效果大大减弱。 6、云台安装:要牢固,转动时无晃动,检查“云台的转动范围”,是否正常,解码器安装在云台附近。</p><h2>网页设计期末复习试题</h2><p>网页设计复习 选择题答案:ABDAC CABCB BACDC CBACA BBDAD CCBBC ACAAC BDDDA DCACB CACDB CACDB DCBAB 如果有同学发现答案有误请大家在群里指出一下 一. 单项选择题 1、HTML 指的是。 A.超文本标记语言(Hyper Text Markup Language) B.家庭工具标记语言(Home Tool Markup Language) C.超链接和文本标记语言(Hyperlinks and Text Markup Language) 2、Web 标准的制定者是万维网联盟(W3C)。 A. 微软(Microsoft) B.万维网联盟(W3C) C.网景公司(Netscape) 3、在下列的 HTML 中,哪个是最大的标题。 A. <h6> B. <head> C. <heading> D. <h1> 4、在下列的 HTML 中,哪个可以插入折行。 A.<br> B.<lb> C.<break> 5、在下列的 HTML 中,哪个可以添加背景颜色。 A.<body color="yellow"> B.<background>yellow</background> C.<body bgcolor="yellow"> 6、产生粗体字的 HTML 标签是。 A.<bold> B.<bb> C.<b> D.<bld> 7、产生斜体字的 HTML 标签是。 A.<i> B.<italics> C.<ii> 8、在下列的 HTML 中,可以产生超链接? A.<a url="https://www.360docs.net/doc/251745843.html,">https://www.360docs.net/doc/251745843.html,</a> B.<a href="https://www.360docs.net/doc/251745843.html,">W3School</a> C.<a>https://www.360docs.net/doc/251745843.html,</a> D.<a name="https://www.360docs.net/doc/251745843.html,">https://www.360docs.net/doc/251745843.html,</a> 9、能够制作电子邮件链接。 A.<a href="xxx@yyy"> B.<mail href="xxx@yyy"> C.<a href="mailto:xxx@yyy"> D.<mail>xxx@yyy</mail> 10、可以在新窗口打开链接。 A.<a href="url" new> B.<a href="url" target="_blank"> C.<a href="url" target="new"> 11、以下选项中,全部都是表格标签。 A.<table><head><tfoot> B.<table><tr><td> C.<table><tr><tt> D.<thead><body><tr> 12、可以使单元格中的内容进行左对齐的正确 HTML 标签是。 A.<td align="left"> B.<td valign="left"></p><h2>a标签target属性详解</h2><p>a标签target属性详解 HTML 标签的target 属性 HTML 标签 定义和用法 标签的target 属性规定在何处打开链接文档。 如果在一个标签内包含一个target 属性,浏览器将会载 入和显示用这个标签的href 属性命名的、名称与这个目标吻合的框架或者窗口中的文档。如果这个指定名称或id 的框架或者窗口不存在,浏览器将打开一个新的窗口,给这个窗口一个指定的标记,然后将新的文档载入那个窗口。从此以后,超链接文档就可以指向这个新的窗 口。 打开新窗口 被指向的超链接使得创建高效的浏览工具变得很容易。例如,一个简单的内容文档的列表,可以将文档重定向到一个单独的窗口: Table of Contents target="view_window">Preface</p><p>target="view_window">Chapter 1 target="view_window">Chapter 2 target="view_window">Chapter 3亲自试一试 当用户第一次选择内容列表中的某个链接时,浏览器将打开一个新的窗口,将它标记为 "view_window",然后在其中显示希望显示的文档内容。如果用户从这个内容列表中选择另一个链接,且这个 "view_window" 仍处于打开状态,浏览器就会再次将选定的文档载入那个窗口,取代刚才的那些文档。 在整个过程中,这个包含了内容列表的窗口是用户可以访问的。通过单击窗口中的一个连接,可使另一个窗口的内容发生变化。 在框架中打开窗口 不用打开一个完整的浏览器窗口,使用target 更通常的方法是在一个显示中将超链接内容定向到一个或者多个框 架中。可以将这个内容列表放入一个带有两个框架的文档的其中一个框架中,并用这个相邻的框架来显示选定的文档: name="view_frame"></p><h2>歌唱《青春舞曲》</h2><p>【课型】歌唱课型 【课题】《青春舞曲》 【教材】上海教育出版社六年级《音乐》教材第一学期第四单元“民族花苑” 【主要教学内容】 1、复习乐曲《马车夫之歌》 2、学唱歌曲《青春舞曲》 【教学任务分析】 1、教材简析 《青春舞曲》是王洛宾根据维吾尔族民歌创编的歌曲。这首歌的歌词用富于哲理的生活现实告诉年轻人:有些事物可以去而复返,有些事物却是一去不复返的。而人的青春正像那鸟儿一样,飞去后即不再回头。这首歌为4/4拍,结构为单乐段结构后缀补充段。歌曲旋律采用重复、变化重复及衍化动机的手法写成。整个歌曲给人以亲切、活泼、充满青春活力的感受。 2、学情分析 六年级的学生经过小学音乐的基础学习,已有了初步的审美能力、浅显音 乐知识与基本的技能,同时对周围事物有了一定的认识,对音乐作品也有了初步的感性认识。但重要的还是要培养他们对音乐的兴趣和热情,注重音乐课基本常规、欣赏音乐和演唱的习惯。同时,教学中应积极引领学生参加各项音乐实践活动,以提高学生的感知、表现、鉴赏、创造等审美能力。 【育人立意】 以人文为主线,以“音乐审美”为核心,以提高学生音乐实践能力为基点,以充分调动学生学习积极性为评价宗旨。中学的音乐教育是在学生已有的小学音乐学习的基础上进行的,中学则应加以巩固和提高,继续拓展学生的音乐视野,加强对生活的感受和理解能力。通过学生参与教学活动的愉悦学习,激发学生的主动探究意识,加深对音乐内涵的理解,更大地调动学生学习积极性和求知欲。 【教学目标】 1、学会用自然圆润的声音,轻松活泼的情绪演唱歌曲《青春舞曲》。正确把 握歌曲的音乐情绪和风格,体会歌曲的旋律特点。</p><p>2、尝试在聆听、模唱、律动中学习歌曲,体验歌曲的音乐情绪,加深对歌 曲 风格的理解。通过各种音乐活动,调动学生的积极参与,培养学生节奏感和创造力,训练协调性。 3、通过学习维吾尔族歌曲《青春舞曲》及其相关知识,培养学生喜欢并热 爱我国的民族音乐。懂得青春易逝的道理,启发学生珍惜光阴,努力学习。 【教学重难点】 教学重点:在听、唱、跳等音乐活动中体验和表现歌曲的情绪,并能用自然圆润的声音演唱歌曲。 教学难点:掌握维吾尔族民歌特点,能准确掌握歌曲节奏型。运用速度、力度的知识对歌曲进行处理,丰富歌曲的表现力。 【教学过程】 一、复习民歌《马车夫之歌》 1、复习民歌《马车夫之歌》。 2、复习巩固切分节奏型。 3、随着音乐节拍,用手鼓为乐曲伴奏,模唱乐曲旋律。 二、新授歌曲《青春舞曲》 (一)人文介绍,维吾尔族风情、文化艺术铺垫 1、维吾尔族简介 2、艺术文化介绍 (二)初听全曲,整体感受 1、教师设问:这首歌曲的音乐情绪是什么?节奏特点怎样? 2、教师设问:演唱形式是怎样的? 3、《青春舞曲》歌曲背景介绍。 (三)学唱歌曲,实践练习 1、学习歌曲中的节奏 1)教师讲授附点音符(十六分、八分)、后十六分音符的读法。 2)引导学生正确掌握附点十六分音符节奏型。 3)掌握全曲正确节奏型。 2、进一步熟悉旋律</p><h2>Dreamweaver里标签及属性详解</h2><p>《》 Dreamweaver里标签及属性的详细解释 Dreamweaver标签库可以帮助我们轻松的找到所需的标签,并根据列出的属性参数使用它,常用的HTML标签和属性解释, 请搜索"常用的HTML标签和属性". 基本结构标签: <HTML>,表示该文件为HTML文件 <HEAD>,包含文件的标题,使用的脚本,样式定义等 <TITLE>---,包含文件的标题,标题出现在浏览器标题栏中 ,的结束标志 ,放置浏览器中显示信息的所有标志和属性,其中内容在浏览器中显示. ,的结束标志 ,的结束标志 其它主要标签,以下所有标志用在中: ,链接标志,"…"为链接的文件地址 ,显示图片标志,"…"为图片的地址
        ,换行标志

        ,分段标志 ,采用黑体字 ,采用斜体字


        ,水平画线
        ,定义表格,HTML中重要的标志
        中 ,定义表格的单元格,用在中 ,字体样式标志 属性: 属性是用来修饰标志的,属性放在开始标志内. 例:属性bgcolor="BLACK"表示背景色为黑色. 引用属性的例子: 表示页面背景色为黑色; 表示表格背景色为黑色. 常用属性: 对齐属性,范围属性: ALIGN=LEFT,左对齐(缺省值),WIDTH=象素值或百分比,对象宽度. ALIGN=CENTER,居中,HEIGHT=象素值或百分比,对象高度. ALIGN=RIGHT,右对齐. 色彩属性:

        《青春舞曲》五年级

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