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Published： 07 September 2020 ，
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周阳,沈爱国,毕大平.2020.双通道SAR振动目标快速检测.遥感学报,24(9): 1143-1156
Zhou Y,Shen A G and Bi D P. 2020. A fast detection algorithm of vibration targets for Dual-Channel SAR. Journal of Remote Sensing(Chinese)，24(9): 1143-1156[DOI：10.11834/jrs.20209004]
目标的振动会对雷达回波产生特殊的相位调制，称为微多普勒效应，能够提供对微动目标检测的有利信息，因此对提高SAR(Synthetic Aperture Radar)系统性能具有重要意义。然而，已有的检测算法存在运算量大、抗杂波噪声能力弱和无法适应多振动目标等问题。针对这些问题，本文提出一种对振动目标检测的新算法。该算法利用相位中心天线偏置DPCA(Displaced Phase Center Antenna )对消技术消除杂波，并沿方位向累加DPCA信号来提高算法的抗噪声能力。由于振动目标SAR方位回波的频谱与脉冲序列具有高度相似性，本文算法选择了检测重复脉冲序列的脉冲重复频率PRI(Pulse Repetition Interval )变换法来实现振动目标的检测。仿真实验表明，本文算法能够在强杂波噪声条件下检测振动目标，同时具有准确振动频率估计性能，甚至当同一个单元存在多个振动目标时，本文算法依然适用。仿真中振动目标检测的计算机运行总时间不超过0.6 s，说明本文算法适用于实时检测，通过与GLRT算法和Hough变换算法运算量的比较，证明了本文算法相比于经典算法具有运算量小，检测速度快的优点。
Target vibration generates special phase modulation of Synthetic Aperture Radar (SAR) echo signals called micro-Doppler effect
which can provide favorable information for micro-motion target detection and recognition. Mining such favorable information is of great significance to improve the performance of SAR systems. At present
research on SAR vibration target detection is still insufficient. The existing detection algorithms have many problems
such as large computational complexity
weak anti-clutter and anti-noise ability
and inability to adapt to multiple vibration targets.
To solve SAR real-time detection of multiple vibration targets under a strong clutter and noise background
this paper proposes a novel vibration target detection algorithm. The detection algorithm uses the Displaced Phase Center Antenna (DPCA) cancellation technique to suppress ground clutter and accumulates DPCA signal along the azimuth direction to improve the anti-noise ability. The frequency spectrum of the SAR azimuth echo of a vibrating target has a high similarity with pulse sequence. Therefore
the pulse repetition interval transform (PRI transform) method of detecting repeated pulse sequences is chosen in this paper to realize the detection of vibration targets. The detection algorithm is a two-step process. The first step is to find the range cell positions of the vibration targets (called the aim range cells). This step mainly uses DPCA technology to eliminate clutter and accumulates the DPCA signal along the azimuth direction. Then
the vibration target range cells are determined by setting an appropriate threshold. The second step is to determine the number of vibration targets in the aim range cells obtained in the first step and estimate their vibration frequencies. This step mainly performs pseudo-pulse processing on the azimuth spectrum of the target range cell and then performs PRI transformation on the pseudo-pulse train to detect the vibration targets and estimate the vibration frequencies. The algorithm can detect the vibration targets under strong clutter and noise conditions
and it has high frequency estimation precision and a small amount of calculation. Even when multiple vibration targets are present in a single range cell
the algorithm is still applicable.
Simulation results prove the correctness and high efficiency of the proposed algorithm. Results show that under the condition of weak Signal-to-Noise Ratio (SNR)
the first step of the detection can determine the vibration target range cells well
and when the SNR>-40 dB
the detection probability of the aim range cell is higher than 95%. In the second step
all the vibration targets in each aim range cell are detected successfully
and the vibration frequency of each vibration target is estimated accurately. Compared with the GLRT algorithm and the Hough transform algorithm
the proposed algorithm has the advantages of small computational complexity and high detection speed. The total computation time of two-step detection does not exceed 0.6 s
which shows that the proposed algorithm is suitable for real-time detection. The quasidata detection results prove that detecting vibration targets in the actual scene is feasible.
The algorithm outperforms previous ones in that it involves a fairly small amount of computation and exhibits better anti-clutter and anti-noise performance. Hence
this algorithm has high practical value for remote sensing vibration targets.
遥感合成孔径雷达(SAR)振动目标SFM信号DPCAPRI变换CFAR
remote sensingSynthetic Aperture Radar (SAR)vibration targetSFM signalDPCAPRI transformationCFAR
Atteia G E and Collins M J. 2013. On the use of compact polarimetry SAR for ship detection. ISPRS Journal of Photogrammetry and Remote Sensing, 80: 1-9 [DOI: 10.1016/j.isprsjprs.2013.01.009http://dx.doi.org/10.1016/j.isprsjprs.2013.01.009]
Ballard D H. 1981. Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition, 13(2): 111-122 [DOI: 10.1016/0031-3203(81)90009-1http://dx.doi.org/10.1016/0031-3203(81)90009-1]
Chang Y L, Huang X T, Zhou H and Zhou Z M. 2009. A new image domain STAP method for multi-channel SAR with array distortion and non-homogeneity environments. Journal of Remote Sensing, 13(2): 238-245
常玉林, 黄晓涛, 周红, 周智敏. 2009. 可适应阵列误差和非均匀杂波环境的多通道SAR图像域STAP方法. 遥感学报, 13(2): 238-245 [DOI: 10.3321/j.issn:1007-4619.2009.02.010http://dx.doi.org/10.3321/j.issn:1007-4619.2009.02.010]
Chen V C, Li F, Ho S S and Wechsler H. 2006. Micro-Doppler effect in radar: phenomenon, model, and simulation study. IEEE Transactions on Aerospace and Electronic Systems, 42(1): 2-21 [DOI: 10.1109/TAES.2006.1603402http://dx.doi.org/10.1109/TAES.2006.1603402]
Cirillo L, Zoubir A and Amin M. 2008. Parameter estimation for locally linear FM signals using a time-frequency Hough transform. IEEE Transactions on Signal Processing, 56(9): 4162-4175 [DOI: 10.1109/TSP.2008.924797http://dx.doi.org/10.1109/TSP.2008.924797]
Clemente C and Soraghan J J. 2012. Vibrating target micro-Doppler signature in Bistatic SAR with a fixed receiver. IEEE Transactions on Geoscience and Remote Sensing, 50(8): 3219-3227 [DOI: 10.1109/TGRS.2011.2180394http://dx.doi.org/10.1109/TGRS.2011.2180394]
Deng B, Wang H Q, Li X, Qin Y L and Wang J T. 2011. Generalised likelihood ratio test detector for micro motion targets in synthetic aperture radar raw signals. IET Radar, Sonar and Navigation, 5(5): 528-535 [DOI: 10.1049/iet-rsn.2010.0223http://dx.doi.org/10.1049/iet-rsn.2010.0223]
Djurović I, Popović-Bugarin V and Simeunovic M. 2017. The STFT-based estimator of micro-Doppler parameters. IEEE Transactions on Aerospace and Electronic Systems, 53(3): 1273-1283 [DOI: 10.1109/TAES.2017.2669741http://dx.doi.org/10.1109/TAES.2017.2669741]
Krishnan S and Rangayyan R M. 1998. Detection of nonlinear frequency-modulated components in the time-frequency plane using an array of accumulators//Proceedings of IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis. Pittsburgh: IEEE: 557-560 [DOI: 10.1109/TFSA.1998.721485http://dx.doi.org/10.1109/TFSA.1998.721485]
Li G and Varshney P K. 2014. Micro-Doppler parameter estimation via parametric sparse representation and pruned orthogonal matching pursuit. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(12): 4937-4948 [DOI: 10.1109/jstars.2014.2318596http://dx.doi.org/10.1109/jstars.2014.2318596]
Li X, Deng B, Qin Y L, Wang H Q and Li Y P. 2011. The influence of target micromotion on SAR and GMTI. IEEE Transactions on Geoscience and Remote Sensing, 49(7): 2738-2751 [DOI: 10.1109/tgrs.2011.2104965http://dx.doi.org/10.1109/tgrs.2011.2104965]
Liang Y, Zhang Q, Luo Y, Bei Y Q and Chen Y A. 2015. Micro-Doppler features analysis and extraction of vibrating target in FMCW SAR based on slow time envelope signatures. IEEE Geoscience and Remote Sensing Letters, 12(10): 2041-2045 [DOI: 10.1109/LGRS.2015.2444658http://dx.doi.org/10.1109/LGRS.2015.2444658]
Nishiguchi K and Kobayashi M. 2000. Improved algorithm for estimating pulse repetition intervals. IEEE Transactions on Aerospace and Electronic Systems, 36(2): 407-421 [DOI: 10.1109/7.845217http://dx.doi.org/10.1109/7.845217]
Noviello C, Fornaro G and Martorella M. 2015. Focused SAR image formation of moving targets based on Doppler parameter estimation. IEEE Transactions on Geoscience and Remote Sensing, 53(6): 3460-3470 [DOI: 10.1109/TGRS.2014.2377293http://dx.doi.org/10.1109/TGRS.2014.2377293]
Oveis A H and Sebt M A. 2017. Coherent method for ground-moving target indication and velocity estimation using Hough transform. IET Radar, Sonar and Navigation, 11(4): 646-655 [DOI: 10.1049/iet-rsn.2016.0262http://dx.doi.org/10.1049/iet-rsn.2016.0262]
Ruegg M, Meier E and Nuesch D. 2007. Vibration and rotation in millimeter-wave SAR. IEEE Transactions on Geoscience and Remote Sensing, 45(2): 293-304 [DOI: 10.1109/tgrs.2006.887025http://dx.doi.org/10.1109/tgrs.2006.887025]
Stankovic L, Dakovic M, Thayaparan T and Popovic-Bugarin V. 2015. Inverse radon transform-based micro-Doppler analysis from a reduced set of observations. IEEE Transactions on Aerospace and Electronic Systems, 51(2): 1155-1169 [DOI: 10.1109/TAES.2014.140098http://dx.doi.org/10.1109/TAES.2014.140098]
Sun Z Q, Li B Z and Lu Y B. 2009. Research on micro-motion and micro-Doppler of ballistic targets//Proceedings of 2009 IET International Radar Conference. Guilin: IET: 1-4 [DOI: 10.1049/cp.2009.0189http://dx.doi.org/10.1049/cp.2009.0189]
Suwa K, Yamamoto K, Tsuchida M, Nakamura S, Wakayama T and Hara T. 2017. Image-based target detection and radial velocity estimation methods for multichannel SAR-GMTI. IEEE Transactions on Geoscience and Remote Sensing, 55(3): 1325-1338 [DOI: 10.1109/tgrs.2016.2622712http://dx.doi.org/10.1109/tgrs.2016.2622712]
Thayaparan T, Abrol S and Riseborough E. 2007. Analysis of radar micro-Doppler signatures from experimental helicopter and human data. IET Radar, Sonar and Navigation, 1(4): 289-299 [DOI: 10.1049/iet-rsn:20060103http://dx.doi.org/10.1049/iet-rsn:20060103]
Wang Q, Pepin M, Beach R J, Dunkel R, Atwood T, Santhanam B, Gerstle W, Doerry A W and Hayat M M. 2012. SAR-based vibration estimation using the discrete fractional Fourier transform. IEEE Transactions on Geoscience and Remote Sensing, 50(10): 4145-4156 [DOI: 10.1109/TGRS.2012.2187665http://dx.doi.org/10.1109/TGRS.2012.2187665]
Yang Q, Deng B, Wang H Q, Qin Y L and Ding W X. 2014. Doppler aliasing free micro-motion parameter estimation algorithm based on the spliced time-frequency image and inverse Radon transform//Proceedings of 2014 International Conference on Information and Communications Technologies. Nanjing: IET: 1-6 [DOI: 10.1049/cp.2014.0607http://dx.doi.org/10.1049/cp.2014.0607]
Zhang W, Tong C M, Zhang Q, Zhang Y C and Zhang X. 2012. Extraction of vibrating features with dual-channel fixed-receiver bistatic SAR. IEEE Geoscience and Remote Sensing Letters, 9(3): 507-511 [DOI: 10.1109/lgrs.2011.2172974http://dx.doi.org/10.1109/lgrs.2011.2172974]
Zhang Y, Fu J B, Mai C Y and Sun J P. 2013. Improved keystone transform algorithm for paired echoes focusing of SAR vibrating targets. Journal of Signal Processing, 29(11): 1457-1462
张远, 付锦斌, 麦超云, 孙进平. 2013. SAR振动目标成对回波聚焦的改进Keystone变换算法. 信号处理, 29(11): 1457-1462
Zhao G H, Fu Y W, Nie L and Fu Y W. 2015. Imaging and micro-Doppler analysis of vibrating target in multi-input–multi-output synthetic aperture radar. IET Radar, Sonar and Navigation, 9(9): 1360-1365 [DOI: 10.1049/iet-rsn.2014.0480http://dx.doi.org/10.1049/iet-rsn.2014.0480]
Zhou Y, Bi D P, Shen A G and Wang X P. 2019. Hough transform-based large micro-motion target detection and estimation in synthetic aperture radar. IET Radar, Sonar and Navigation, 13(4): 558-565 [DOI: 10.1049/iet-rsn.2018.5407http://dx.doi.org/10.1049/iet-rsn.2018.5407]
Zhu S, Mohammad-Djafari A, Wang H Q, Deng B, Li X and Mao J J. 2012. Parameter estimation for SAR micromotion target based on sparse signal representation. EURASIP Journal on Advances in Signal Processing, 2012(1): 13 [DOI: 10.1186/1687-6180-2012-13http://dx.doi.org/10.1186/1687-6180-2012-13]
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