Shape Parameter Estimation of Radar K-distributed Sea Clutter Based on Support Vector Regression and Percentiles
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摘要: 针对传统的雷达K分布海杂波形状参数估计方法在异常样本存在情况下估计精度严重下降的问题，该文提出一种基于支持向量回归(SVR)和样本分位数比值的K分布海杂波形状参数估计方法。首先给定K分布杂波参数和分位数位置的值，根据K分布的累积分布函数计算样本分位数比值及其对数，然后建立以样本分位数比值对数为输入、待估计形状参数为输出的SVR模型，通过交叉验证确定SVR模型的超参数，最后训练SVR模型实现对K分布海杂波形状参数的稳健精确估计。仿真和实测雷达数据表明，所提方法的估计误差低于基于矩的估计方法的估计误差，并且与基于分位数的估计方法具有相近估计性能。此外，相比已有基于分位数的方法，所提方法的超参数容易确定，并且不依赖于查表。Abstract: In order to solve the problem that the estimation accuracy of traditional methods for estimating the shape parameters of radar K-distributed sea clutter is seriously degraded when there are outliers, a method for estimating the shape parameters of radar K-distributed sea clutter based on Support Vector Regression (SVR) and sample percentile ratio is proposed in this paper. Firstly, the clutter parameters and the percentile ranks are given, the sample percentile ratio and its logarithm are calculated according to the cumulative distribution function of the K distribution, and then an SVR model with the logarithm of the sample quantile ratio as input and the shape parameters to be estimated as output is established. The hyperparameters of SVR model are determined by cross-validation, and finally the SVR model is trained to estimate the shape parameter of K-distributed sea clutter robustly and accurately. The simulated and measured radar data show that the estimation error of the proposed method is lower than that of the conventional moment-based methods, and its estimation performance is similar to that of the percentile-based methods. Moreover, compared with the existing percentile-based methods, the hyperparameters of the proposed method are easy to determine, and it does not depend on table lookup.
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[1] 丁昊, 董云龙, 刘宁波, 等. 海杂波特性认知研究进展与展望[J]. 雷达学报, 2016, 5(5): 499–516. doi: 10.12000/JR16069.DING Hao, DONG Yunlong, LIU Ningbo, et al. Overview and prospects of research on sea clutter property cognition[J]. Journal of Radars, 2016, 5(5): 499–516. doi: 10.12000/JR16069. [2] 刘宁波, 姜星宇, 丁昊, 等. 雷达大擦地角海杂波特性与目标检测研究综述[J]. 电子与信息学报, 2021, 43(10): 2771–2780. doi: 10.11999/JEIT200451.LIU Ningbo, JIANG Xingyu, DING Hao, et al. Summary of research on characteristics of radar sea clutter and target detection at high grazing angles[J]. Journal of Electronics & Information Technology, 2021, 43(10): 2771–2780. doi: 10.11999/JEIT200451. [3] 张玉石, 李笑宇, 张金鹏, 等. 基于深度学习的海杂波谱参数预测与影响因素分析[J]. 雷达学报, 2023, 12(1): 110–119. doi: 10.12000/JR22133.ZHANG Yushi, LI Xiaoyu, ZHANG Jinpeng, et al. Sea clutter spectral parameters prediction and influence factor analysis based on deep learning[J]. Journal of Radars, 2023, 12(1): 110–119. doi: 10.12000/JR22133. [4] WARD K D. Compound representation of high resolution sea clutter[J]. Electronics Letters, 1981, 17(16): 561–563. doi: 10.1049/el:19810394. [5] WEINBERG G V. Assessing pareto fit to high-resolution high-grazing-angle sea clutter[J]. Electronics Letters, 2011, 47(8): 516–517. doi: 10.1049/el.2011.0518. [6] MEZACHE A, SOLTANI F, SAHED M, et al. Model for non-rayleigh clutter amplitudes using compound inverse gaussian distribution: An experimental analysis[J]. IEEE Transactions on Aerospace and Electronic Systems, 2015, 51(1): 142–153. doi: 10.1109/TAES.2014.130332. [7] CARRETERO-MOYA J, GISMERO-MENOYO J, BLANCO-DEL-CAMPO Á, et al. Statistical analysis of a high-resolution sea-clutter database[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(4): 2024–2037. doi: 10.1109/TGRS.2009.2033193. [8] SHUI Penglang, LIU Ming, and XU Shuwen. 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. [9] ZHANG Yichen and SHUI Penglang. Antenna beampattern matched optimum coherent detection in high-resolution mechanically scanning maritime surveillance radars[J]. IEEE Transactions on Aerospace and Electronic Systems, 2023, 59(3): 2764–2779. doi: 10.1109/TAES.2022.3218607. [10] 张坤, 水鹏朗, 王光辉. 相参雷达K分布海杂波背景下非相干积累恒虚警检测方法[J]. 电子与信息学报, 2020, 42(7): 1627–1635. doi: 10.11999/JEIT190441.ZHANG Kun, SHUI Penglang, and WANG Guanghui. Non-coherent integration constant false alarm rate detectors against K-distributed sea clutter for coherent radar systems[J]. Journal of Electronics & Information Technology, 2020, 42(7): 1627–1635. doi: 10.11999/JEIT190441. [11] 张坤, 水鹏朗. 广义Pareto分布海杂波背景下非相干检测器恒虚警性能分析[J]. 电子与信息学报, 2021, 43(3): 523–530. doi: 10.11999/JEIT200644.ZHANG Kun and SHUI Penglang. CFAR analysis of non-coherent detectors in generalized pareto distributed sea clutter[J]. Journal of Electronics & Information Technology, 2021, 43(3): 523–530. doi: 10.11999/JEIT200644. [12] JOUGHIN I R, PERCIVAL D B, and WINEBRENNER D P. Maximum likelihood estimation of K distribution parameters for SAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 1993, 31(5): 989–999. doi: 10.1109/36.263769. [13] SHUI Penglang, ZOU Pengjia, and FENG Tian. Outlier-robust truncated maximum likelihood parameter estimators of generalized pareto distributions[J]. Digital Signal Processing, 2022, 127: 103527. doi: 10.1016/j.dsp.2022.103527. [14] TIAN Chao and SHUI Penglang. Outlier-robust truncated maximum likelihood parameter estimation of compound-gaussian clutter with inverse gaussian texture[J]. Remote Sensing, 2022, 14(16): 4004. doi: 10.3390/rs14164004. [15] ISKANDER D R and ZOUBIR A M. Estimation of the parameters of the K-distribution using higher order and fractional moments [radar clutter][J]. IEEE Transactions on Aerospace and electronic systems, 1999, 35(4): 1453–1457. doi: 10.1109/7.805463. [16] YU Han, SHUI Penglang, and HUANG Yuting. Low-order moment-based estimation of shape parameter of CGIG clutter model[J]. Electronics Letters, 2016, 52(18): 1561–1563. doi: 10.1049/el.2016.2248. [17] BLACKNELL D and TOUGH R J A. Parameter estimation for the K-distribution based on [ z log( z)][J]. IEE Proceedings-Radar, Sonar and Navigation, 2001, 148(6): 309–312. doi: 10.1049/ip-rsn:20010720. [18] SHUI Penglang, SHI Lixiang, 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. [19] SHUI Penglang, YU Han, SHI Lixiang, et al. Explicit bipercentile parameter estimation of compound-Gaussian clutter with inverse gamma distributed texture[J]. IET Radar, Sonar & Navigation, 2018, 12(2): 202–208. doi: 10.1049/iet-rsn.2017.0174. [20] YU Han, SHUI Penglang, LU Kai, et al. Bipercentile parameter estimators of bias reduction for generalised pareto clutter model[J]. IET Radar, Sonar & Navigation, 2020, 14(7): 1105–1112. doi: 10.1049/iet-rsn.2019.0622. [21] YU Han, SHUI Penglang, and LU Kai. Outlier-robust tri-percentile parameter estimation of K-distributions[J]. Signal Processing, 2021, 181: 107906. doi: 10.1016/j.sigpro.2020.107906. [22] XUE Jian, SUN Mengling, LIU Jun, et al. Shape parameter estimation of K-distributed sea clutter using neural network and multisample percentile in radar industry[J]. IEEE Transactions on Industrial Informatics, 2023, 19(6): 7602–7612. doi: 10.1109/TII.2022.3211321. [23] SHI Sainan, GAO Jijuan, CAO Ding, et al. Self-learning parameter estimation of K-distributed clutter using GRU network[J]. IEEE Geoscience and Remote Sensing Letters, 2023, 20: 1–5. doi: 10.1109/LGRS.2023.3323294. [24] AWAD M and KHANNA R. Support vector regression[M]. AWAD M and KHANNA R. Efficient learning machines: Theories, Concepts, and Applications for Engineers and System Designers. Berkeley: Apress, 2015: 67–80. doi: 10.1007/978-1-4302-5990-9_4. [25] 于涵. 海杂波稳健参数估计方法研究[D]. [博士论文], 西安电子科技大学, 2020: 21–27. doi: 10.27389/d.cnki.gxadu.2020.003363.YU Han. Research on robust parameter estimation methods of sea clutter[D]. [Ph. D. dissertation], Xidian University, 2020: 21–27. doi: 10.27389/d.cnki.gxadu.2020.003363. [26] ABO-KHALIL A G and LEE D C. MPPT control of wind generation systems based on estimated wind speed using SVR[J]. IEEE Transactions on Industrial Electronics, 2008, 55(3): 1489–1490. doi: 10.1109/TIE.2007.907672. [27] 刘宁波, 丁昊, 黄勇, 等. X 波段雷达对海探测试验与数据获取年度进展[J]. 雷达学报, 2021, 10(1): 173–182. doi: 10.12000/JR21011.LIU Ningbo, DING Hao, HUANG Yong, et al. Annual progress of the sea-detecting X-band radar and data acquisition program[J]. Journal of Radars, 2021, 10(1): 173–182. doi: 10.12000/JR21011. [28] HERSELMAN P L R. CSIR fynmeet sea clutter measurement trial: Datasets[EB/OL]. https://researchspace.csir.co.za/dspace/handle/10204/1847?show=full, 2006. [29] HAYKIN S. The McMaster IPIX radar sea clutter database[EB/OL]. http://soma.ece.mcmaster.ca/ipix/, 1998. -
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