Polarimetric extraction technique of atmospheric targets based on double sLdr and morphology

Polarimetric Extraction Technique of Atmospheric Targets Based on Double sLdr and Morphology Mi HE, Yongjian Nian, Xuesong WANG, Yongzhen LI, Shunping XIAO

School of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China

hmcherry@https://www.360docs.net/doc/d66672598.html,

Abstract—An extraction method of the polarimetric atmospheric target has been proposed to obtain most useful information with noise, clutter and artificial signals as less as possible for dual-orthogonal frequency modulation continuous wave (FMCW) polarimetric radar. The signal processing is described for dual-orthogonal FMCW polarimetric radar. The polarimetric range-Doppler spectra of precipitation obtained in a single sweep time are calibrated by radar constant calibration and reciprocity modification. The mask matrix for filtering is constructed based on the double spectral linear depolarization ratios (sLdr) and mathematical morphology methods. The precipitation atmospheric targets are extracted successfully by multiplying the mask matrix with the measured range-Doppler spectra by the polarimetric agile radar S- And X-band (PARSAX) radar. The proposed technique keeps more atmospheric targets and suppresses more clutter, noise and artificial signals compared with the original data and the data after double sLdr filtering combined with noise clipping.

Keywords- Spectral linear depolarization ratios (sLdr), Simultaneous measurement, Radar polarimetry, Clutter suppression, Atmospheric target extraction

I.I NTRODUCTION

PARSAX radar in Delft University of Technology can measure scattering matrixes of targets in one sweep time using the simultaneous scheme. However in the polarimetric switching scheme, the maximum unambiguous Doppler velocity is decreased and the phase difference between two columns of scattering matrix is produced[1]. No matter which elevation the radar points, noise, ground clutter, clutter of moving targets (like birds, vehicles and airplanes) and artificial signals (caused by non-ideal hardware) always exist in high-resolution polarimetric range-Doppler spectra and contaminate the atmospheric targets which are our interested items.

The atmospheric targets need to be extracted for further analysis like the microphysical parameters estimation such as shapes and orientations of targets [2-3]. The spectral polarimetric clutter suppression filter can reduce erroneous and missing data as well as computational burden[4]. The ground clutter suppression technique (like notch filter[5]) will cause some loss of atmospheric target information with velocity around 0 m/s. The spectral cross-correlation coefficient filter[6], the polarimetric de-aliasing filter, the spectral differential reflectivity technique[7] have been proposed in recent years to suppress clutter on the polarimetric range-Doppler spectra. In [8] the double spectral linear depolarization ratios filter can suppress clutter and artificial signals more efficiently than the spectral linear depolarization ratio filter, the spectral cross-correlation coefficient filter[6], the polarimetric de-aliasing filter and the spectral differential reflectivity technique[7] when most useful information is kept. However, a small quantity of clutter still can’t be eliminated and a few of atmospheric targets are removed using the double sLdr filtering combined with the noise clipping. In this study, an improved technique is proposed to extract atmospheric targets from the calibrated range-Doppler spectra with noise, different types of clutter and artificial signals by constructing a mask matrix on double sLdr and mathematical morphology methods without noise clipping. The mask matrix can filter out almost all clutter, noise and artifact signals to extract clear atmospheric targets efficiently from the original range-Doppler spectra.

The paper consists of four sections. Section 2 describes the signal processing of the dual-orthogonal FMCW radar in simultaneous measurements to obtain the range-Doppler spectrogram. Section 3 gives the atmospheric target extraction technique which can suppress noise, artificial signals and clutter. Section 4 shows the real data processing results and comparison. Section 5 draws the conclusion.

II.S IGNAL PROCESSING FOR DUAL-ORTHOGONAL FMCW

RADAR

A.Range-Doppler spectra

The typical flow chart of the raw range-Doppler spectra at the output of dual-orthogonal polarimetric Doppler radar processor is presented in Fig.1. The high resolution range-Doppler spectra of atmospheric targets can be obtained with sweep

N sweeps received data. Firstly, the positive and negative LFM continuous wave signals are transmitted from Horizontal and Vertical polarimetric channels simultaneously. Then the simultaneously received signals are multiplied with reference signals (namely replicas of the transmitted signals) to perform the range compression. The multiplication results pass the designed low pass filter (LPF) and become beat signals. The polarimetric cross-channel interference needs be suppressed before the FFT in the dual-orthogonal polarization radar signal processing in simultaneous measurement scheme[9]. Finally, a two-dimensional (range and velocity dimensional) FFT is

implemented on the sampling of beat signals of

sweep

N sweeps to get the raw range-Doppler spectrum which is shown as spectrogram. The detail of signal processing for the dual-orthogonal FMCW polarimetric radar is described in [9].

(a) Flow chart of range spectrum

Fig. 1. Block-diagram of the PARSAX polarimetric CW radar with dual-orthogonal sounding signals in simultaneous measurement scheme

After the raw range-Doppler spectrum is obtained, the radar calibration should be implemented. Firstly the noise is measured in the passive mode of radar with transmitted power switched off for a few minutes. Then the calibration factor is built by comparing the averaged measured noise reflectivity with the computed theoretical noise reflectivity[10]. After the radar constant calibration, the reflectivity converted from quantized value has standard reflectivity unit dBZ. At last the reflectivity in cross-polar channels should be modified according to the scatter reciprocity[11].

The calibrated raw range-Doppler spectra are still very noisy and need further smoothing to remove most of the statistical variations while keeping the main trend of each spectrum. Detail of such smoothing is studied in [12].

III.P OLARIMETRIC A LGORITHM FOR THE A TMOSPHERIC

T ARGET D ETECTION

Atmospheric targets have to be detected on range-Doppler spectrograms for further radar data processing and atmospheric targets parameters estimation.The noise clipping technique[13] is usually used for targets detection of atmospheric Doppler radars. The main problem of noise clipping approach is the optimal selection of additional threshold value, which is added to the averaged noise level. The adding threshold usually underestimates weak targets[10]. So the noise clipping is avoided in the proposed method. However, the double sLdr filtering in [8] needs the noise clipping to suppress most noise on the background while some useful atmospheric targets are removed at the same time. A.Double spectral linear depolarization ratios filter

The spectral linear depolarization ratios can be defined as:

,10log

10log

HV r HV r

dr r

VV r VV r

VH r VH r

VV r VV r

s r v s r v

r v

s r v s r v

,10log

10log

VH r VH r

dr r

HH r HH r

HV r HV r

HH r HH r

s r v s r v

r v

s r v s r v

sL (2) where

XY r

s r v is the scattering matrix element with range r

and velocity, angular brackets < > represent the averaging,

which can be implemented in time, space or Doppler domain.

The observed values of spectral linear depolarization ratios

for most precipitation phenomena and clouds even in cases

with low signal to noise ratios are smaller than the threshold

value which equals to -5dB, according to the statistical characteristics analysis of the large atmospheric target database

collected during long-term multi-year measurements in different meteorological conditions[8]. As shown in early tests,

the double sLdr filtering algorithm, which uses this threshold

value for the sLdr, can be especially useful for the suppression

of clutter and the internal/external interference signals of radar system.

However, the simple threshold-based detection algorithm,

namely double sLdr filtering, removes parts of atmospheric signals. One reason is that not all the sLdr values of atmospheric targets are smaller than -5dB. The other reason is combination of noise clipping will suppress some weak atmospheric targets. The loss parts of atmospheric targets are

shown as a presence of “holes” inside the continuous area of atmospheric targets. From physical point of view, the atmospheric targets like precipitation have continuous distribution in range-Doppler spectrograms.

B.Construciton of morphologic target mask

The further processing of “morphologic target mask”

should decrease such non-natural discontinuities. As the double

sLdr filter can be thought as a binary mask matrix dot-multiplied with the original range-Doppler spectra, the proposed method is described to improve the binary mask

matrix called original mask matrix. Firstly, the noise residuals

which occupy separated pixels, should be removed resort to

majority black operator[14]. Majority black operator, which is

based on hit-or-miss transformations, is useful to for filling

small holes in objects and remove separated noise. The standard majority black operator often adopts squared structuring element with size 3-by-3[14]. The decision criterion

is that current “target mask” pixel value is set to 1 if at lest 5

pixels neighbored the current pixel (defined by structuring elements) equal to 1. Otherwise, set the current pixel to 0. The application of majority black operator removes almost all noise

pixels but still can’t recover missing precipitation data completely. Next the dilation is operated to restore initial

interior of “target mask” further. The disk-shaped structuring

element with radius 3 is adopted. The dilation operation will close the gaps inside the target region but extend the region’s perimeter. Then the erosion with the same size structuring element can restore the perimeter to original size. The combination of first dilation and then erosion is defined as the closing operator, which can smooth the bitmap image by filling up the gaps and keep the perimeter of atmospheric targets based on the assumption that atmospheric targets have continuous shape on the range-Doppler spectrogram.

As such algorithm for atmospheric targets detection has to be implemented for many applications in real time, the reduction of algorithm’s computational cost is quite important. Using a priori information about typical behavior of target’s signals on current radar scene can seriously reduce computational cost. For example, in case of precipitation observations with near vertically pointed antennas, it is negligible that the detective probability of precipitation targets with negative Doppler velocity less than -15m/s or any target above some height level defined by the radar sensitivity. So these regions of cells on the “target mask” can be simply set to zero or excluded from processing in detection algorithm.

The “morphologic target mask” can be used as filter via

spectrograms in all polarimetric channels after estimation,. Three moments of range-Doppler spectra: the integrated reflectivity, the mean Doppler velocity, and the spectral Doppler width, are usually calculated [10] for atmospheric targets. Some examples of improvement will be illustrated in the next section.

IV.R ESULTS OF EXPERIMENTAL DATA AND COMPARISON The experimental data for this study were collected using the PARSAX radar. The shower on June 30 and the rain on July 14 were measured with vertically pointed antenna system. The rain on July 12 was measured using azimuthally scanning mode (during measurement antennas rotated from -5o to 355o of geographical azimuth) with 75o of elevation. The noise measurement on June 25th lasted 2 minutes. The clear-air measurement was on July 9th with antenna vertically pointing. The transmitted power in all measurements except noise measurement was set to 40dBm.

The raw range-Doppler spectrogram of the precipitation after calibration is shown in Fig.2, which was measured on July 12. The reflectivity of precipitation calculated in resolution volume is expressed in dBZ unit. The precipitation has strong co-polar component and weak cross-polar component with lots of noise existing on the background. The filtered spectrograms by double sLdr filter only are presented in Fig.3. The double sLdr filter suppresses more clutter, noise, and artificial signals than single sLdr filter[8]. At the same time, more precipitation signals are removed by the double sLdr filtering. After the terminal morphological mask matrix is obtained, the atmospheric target can be extracted from the background simply by dot-multiplying the original range-Doppler spectrum with the terminal mask matrix. From Fig.4, a clear precipitation is extracted successfully almost without clutter, artifacts and noise. The reasonable and intact atmospheric targets are presented with no ground clutter on the Doppler bin 0m/s and its neighboring Doppler bins. The redundant tail of precipitation below 300m is in the near field of radar antennas, which is not the interesting part for precipitation at present, as the focus is to obtain good results in the radar far field firstly.

Fig.5 presents the Doppler width of the precipitation after different filtering methods changes with time at elevation angle 90o on June 30, 2010 in Delft. The adding threshold of noise clipping for double sLdr filtering in Fig.5(b) is chosen as 9dB which can just reduce almost all the noise on the background of range-Doppler spectrograms for PARSAX radar. The big value in Doppler width probably corresponds to clutter, as the Doppler width is sensitive to clutter. The double sLdr filter can reduce most ground clutter and artificial signals in the profile. However, it fails to suppress the noise and clutter on the right part with colorful pixels of the profile around 1 km, and removes some useful precipitation data on the right part of the profile around 3 km and 5 km and on the left part of the profile around 1 km and 5 km at the same time Velocity, m/s

-20-1001020

-60

-40

-20

Velocity, m/s

-20-1001020

-60

-40

-20

Fig. 2. Raw range-Doppler spectrograms in HH (left) and HV (right)

channels after radar constant calibration

Velocity, m/s

-20-1001020

-60

-40

-20

Velocity, m/s

-20-1001020

-60

-40

-20

Fig. 3. Filtering results in HH (left) and HV (right) channels using double spectral linear depolarization ratios without noise clipping

Velocity, m/s

-20-1001020

-60

-40

-20

Velocity, m/s

-20-1001020

-60

-40

-20

Fig. 4. The range-Doppler spectrogram after filtering using the improved mask matrix in HH (left) and HV (right) channels

A CKNOWLEDGMENT

Original Doppler width (m/s)

Time (s)

100

120

140

4680

1234(a)

Doppler width after double sLdr filtering(m/s)

Time (s)

The authors sincerely thank all colleagues in IRCTR, such as Oleg Krasnov, Christine Unal, Prof. Leo P. Ligthart, Zongbo Wang, Zhijian Li, W.F. van der Zwan, P. Hakkaart and J.H. Zijderveld. And the author HE Mi also acknowledges the Chinese Scholarship Council which offers a scholarship for one-year study abroad.

4Fig. 5. The Doppler width profiles of precipitation (June 30, 2010) in HH channel. (a) is the original data, (b) is the result after noise clipping and double sLdr filtering, (c) is the result processed by the improved mask matrix filtering.

V.C ONCLUSION

For the dual-orthogonal polarimetric FMCW radar, an improved atmospheric target extraction technique has been proposed in this paper based on double sLdr filtering and mathematical morphology methods. The experimental results give the case with simultaneous transmission and reception measurement. The proposed method is valid for the case with alternative transmission or reception. From the terminal extraction results, we can see the proposed method is very efficient to suppress the clutter, noise and artificial signals. At the same time, it maintains most of the atmospheric targets. The precipitation can be detected from single raw range-Doppler spectrograms or averaged spectrogram. The reflectivity, mean Doppler velocity and Doppler width by the proposed method keep more precipitation data and with less clutter and artificial signals than using the double sLdr filtering combined with the noise clipping. The proposed method offers a good basis for microphysical parameters estimation.

However the proposed method needs more study on the analysis of the distribution characteristics of sLdr of clutter and precipitation. And the optimal size of structuring element for closing operator needs be determined in the future as well.

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