Whitening Degree Evaluation Method to Test Estimate Accuracy of Speckle Covariance Matrix

Yu Han; Shui Penglang; Yang Chunjiao; Shi Sainan

doi:10.12000/JR16146

Volume 6 Issue 3

Jun. 2017

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Article Navigation > Journal of Radars > 2017 > 6(3): 285-291

Yu Han, Shui Penglang, Yang Chunjiao, Shi Sainan. Whitening Degree Evaluation Method to Test Estimate Accuracy of Speckle Covariance Matrix[J]. Journal of Radars, 2017, 6(3): 285-291. doi: 10.12000/JR16146

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Yu Han, Shui Penglang, Yang Chunjiao, Shi Sainan. Whitening Degree Evaluation Method to Test Estimate Accuracy of Speckle Covariance Matrix[J]. Journal of Radars, 2017, 6(3): 285-291. doi: 10.12000/JR16146

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Whitening Degree Evaluation Method to Test Estimate Accuracy of Speckle Covariance Matrix

DOI: 10.12000/JR16146

National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China

Funds: The National Natural Science Foundation of China (61671357)

Received Date: 2016-12-16
Rev Recd Date: 2017-04-24

Available Online: 2017-05-27

Publish Date: 2017-06-28

Abstract

Abstract

In the background of sea clutter, the accuracy of adaptive target detection is heavily influenced by the estimated performance of speckle covariance matrix. Generally, Normalized Frobenius Norm (NFN) is used to test the estimated accuracy of different speckle covariance matrix estimators, in which the requirement of a known real covariance matrix is hardly realized in the radar system. Therefore, in this study, a whitening degree evaluation method is proposed wherein the decorrelation of speckle covariance matrix in whitening filter processing of the radar system is fully exploited. It considers the correlation degree among pulses in the whitening clutter vector as the criterion to evaluate the estimate error of the speckle covariance matrix. The proposed method shows consistent conclusions with NFN on simulated data and also avoids limitations of the latter method in real data processing.
- Covariance matrix estimation,
- Whitening degree evaluation,
- Normalized Frobenius Norm (NFN),
- Real covariance matrix

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References(18)

References

[1]	Conte E and Longo M. Characterisation of radar clutter as a spherically invariant random process[J]. IEE Proceedings F-Communications, Radar and Signal Processing, 1987, 134(2): 191–197. DOI: 10.1049/ip-f-1:19870035.
[2]	Rangaswamy M, Weiner D D, and Ozturk A. Non-Gaussian random vector identification using spherically invariant random processes[J]. IEEE Transactions on Aerospace and Electronic Systems, 1993, 29(1): 111–124. DOI: 10.1109/7.249117.
[3]	Pulsone N B. Adaptive signal detection in non-Gaussian interference[D]. [Ph.D. dissertation], Northeastern University, 1997.
[4]	Raghavan R S and Pulsone N B. A generalization of the adaptive matched filter receiver for array detection in a class of non-Gaussian interference[C]. Proceedings of the Adaptive Sensor Array Processing (ASAP) Workshop, Lexington, MA, USA, Mar. 1996: 499–517.
[5]	Conte E, Lops E, and Ricci G. Adaptive radar detection in compound-Gaussian clutter[C]. Proceedings of the European Signal Processing Conference, Edinburgh, Scotland, UK, Sep. 1994.
[6]	何友, 简涛, 苏峰, 等. 非高斯杂波协方差矩阵估计方法及CFAR特性分析[J]. 中国科学: 信息科学, 2011, 41(1): 90–99. http://www.cnki.com.cn/Article/CJFDTOTAL-PZKX201101009.htm He You, Jian Tao, Su Feng, et al.. CFAR assessment of covariance matrix estimators for non-Gaussian clutter[J]. Scientia Sinica Informationis, 2011, 41(1): 90–99. http://www.cnki.com.cn/Article/CJFDTOTAL-PZKX201101009.htm
[7]	孙艳丽, 谢宁波. 基于实测数据的单元平均CFAR检测器性能分析[J]. 兵器装备工程学报, 2016, 37(10): 84–87. DOI: 10.11809/scbgxb2016.10.017. Sun Yan-li and Xie Ning-bo. Performance analysis of cell average CFAR detector based on measured data[J]. Journal of Sichuan Ordnance, 2016, 37(10): 84–87. DOI: 10.11809/scbgxb2016.10.017.
[8]	Gini F and Greco M. Covariance matrix estimation for CFAR detection in correlated heavy tailed clutter[J]. Signal Processing, 2002, 82(12): 1847–1859. doi: 10.1016/S0165-1684(02)00315-8
[9]	Pascal F, Chitour Y, Ovarlez J P, et al.. Covariance structure maximum-likelihood estimates in compound Gaussian noise: Existence and algorithm analysis[J]. IEEE Transactions on Signal Processing, 2008, 56(1): 34–48. DOI: 10.1109/TSP.2007.901652.
[10]	Anastassopoulos V, Lampropoulos G A, Drosopoulos A, et al.. High resolution radar clutter statistics[J]. IEEE Transactions on Aerospace and Electronic Systems, 1999, 35(2): 43–60. DOI: 10.1109/7.745679.
[11]	Ward K D, Baker C J, and Watts S. Maritime surveillance radar. Part1: Radar scattering from the ocean surface[J]. IEE Proceedings F-Radar and Signal Processing, 1990, 137(2): 51–62. doi: 10.1049/ip-f-2.1990.0009
[12]	Zhou Jie, Chen Dong, and Sun Dewei. K distribution sea clutter modeling and simulation based on ZMNL[C]. Proceedings of the 2015 8th International Conference on Intelligent Computation Technology and Automation, Nanchang, China, Jun. 2015: 506–509. DOI: 10.1109/ ICICTA.2015.279.
[13]	谢洪森, 邹鲲, 杨春英, 等. 海杂波协方差矩阵估计及其对目标检测性能的影响[J]. 系统工程与电子技术, 2011, 33(10): 2174–2178. DOI: 10.3969/j.issn.1001-506X.2011.10.06. Xie Hong-sen, Zou Kun, Yang Chun-ying, et al.. Sea clutter covariance matrix estimation and its impact on signal detection performance[J]. Systems Engineering and Electronics, 2011, 33(10): 2174–2178. DOI: 10.3969/ j.issn.1001-506X.2011.10.06.
[14]	Shui Peng-lang, Liu Ming, and Xu Shu-wen. Shape-parameter-dependent coherent radar target detection in k-distributed clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2016, 52(1): 451–465. DOI: 10.1109/ TAES.2015.140109.
[15]	Jansson M and Ottersten B. Structured covariance matrix estimation: A parametric approach[C]. Proceedings of the 2000 IEEE International Acoustics, Speech, and Signal Processing, Istanbul, Turkey, Jun. 2000, 5: 3172–3175.
[16]	Conte E, Lops M, and Ricci G. Adaptive detection schemes in compound-Gaussian clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 1998, 34(4): 1058–1069. DOI: 10.1109/7.722671.
[17]	Shui Peng-lang, Shi Li-xiang, Yu Han, et al.. Iterative maximum likelihood and outlier-robust bipercentile estimation of parameters of compound-Gaussian clutter with inverse Gaussian texture[J]. IEEE Signal Processing Letters, 2016, 23(11): 1572–1576. DOI: 10.1109/LSP. 2016.2605129.
[18]	宋运忠, 杨丽英. 基于L₁范数最小化的逆协方差矩阵估计[J]. 河南师范大学学报(自然科学版), 2016, 44(5): 8–19. DOI: 10.16366/j.cnki.1000-2367.2016.05.002. Song Yun-zhong and Yang Li-ying. A approach to precision matrix estimation based on L₁ norm minimization[J]. Journal of Henan Normal University (Natural Science Edition), 2016, 44(5): 8–19. DOI: 10.16366/j.cnki.1000-2367.2016.05.002.