均匀和部分均匀杂波中子空间目标的斜对称自适应检测方法

丁昊 薛永华 黄勇 关键

引用本文:
Citation:

均匀和部分均匀杂波中子空间目标的斜对称自适应检测方法

    作者简介: 丁昊(1988-),男,博士研究生,主要研究方向为海杂波特性认知、雷达目标检测等。E-mail:hao3431@tom.com;薛永华(1985-),男,博士研究生,主要研究方向为MIMO天波雷达电离层信道仿真、海杂波仿真、波形设计、目标检测等。E-mail:xueyhchina@163.com;黄勇(1979-),男,博士,主要研究方向为雷达目标检测、海杂波特性分析等。E-mail:huangyong_2003@163.com;关键(1968-),男,教授,博士生导师,主要研究方向为雷达目标检测与跟踪、侦察图像处理和信息融合。获全国优秀博士学位论文奖,新世纪百千万人才工程国家级人选。E-mail:guanjian96@tsinghua.org.cn.
    通讯作者: 丁昊, hao3431@tom.com
  • 基金项目:

    国家自然科学基金(61179017, 61201445)和国家自然科学基金青年科学基金(61401495)资助课题

Persymmetric Adaptive Detectors of Subspace Signals in Homogeneous and Partially Homogeneous Clutter

    Corresponding author: Ding Hao, hao3431@tom.com ;
  • 摘要: 在雷达目标的自适应检测领域, 当参考单元数不足时, 充分挖掘协方差矩阵的结构信息是有效提高检测性能的途径之一。为此, 针对多维子空间目标的检测问题, 该文在协方差矩阵关于次对角线具有斜对称结构的约束下, 分别基于一步和两步广义似然比(GLRT), 推导了均匀和部分均匀杂波中的斜对称自适应检测器。由于检测器在设计阶段利用了协方差矩阵的结构信息, 仿真结果表明, 与已有检测器相比, 在参考单元数不足时, 斜对称自适应检测器可明显改善检测性能。此外, 分别从协方差估计方法的影响、目标子空间维数的影响、目标子空间失配性能以及目标起伏的影响4个方面对检测性能进行了仿真分析。
  • [1] Gini F and Farina A. Vector subspace detection in compound-Gaussian clutter, part I: surgey and new results[J]. IEEE Transactions on Aerospace and Electronic Systems, 2002, 38(4): 1295-1311.
    [2] Kelly E J. An adaptive detection algorithm[J]. IEEE Transactions on Aerospace and Electronic Systems, 1986, 22(2): 115-127.
    [3] Robey F C, Fuhrman D L, Kelly E J, et al.. A CFAR adaptive matched filter detector[J]. IEEE Transactions on Aerospace and Electronic Systems, 1992, 28(1): 208-216.
    [4] Kraut S, Scharf L L, and McWhorter L T. Adaptive subspace detectors[J]. IEEE Transactions on Signal Processing, 2001, 49(1): 1-16.
    [5] Kraut S and Scharf L L. The CFAR adaptive subspace detector is a scale-invariant GLRT[J]. IEEE Transactions on Signal Processing, 1999, 47(9): 2538-2541.
    [6] Kraut S, Scharf L L, and ButlerR W. The adaptive coherent estimator: a uniformly most-powerful-invariant adaptive detection statistic[J]. IEEE Transactions on Signal
    [7] Processing, 2005, 53(2): 427-438. Conte E, Lops M, and Ricci G. Asymptotically optimum radar detection in compound-Gaussian clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 1995, 31(2): 617-625.
    [8] Conte E, Lops M, and Ricci G. Adaptive matched filter detection in spherically invariant noise[J]. IEEE Signal Processing Letters, 1996, 3(8): 248-250.
    [9] Gini F. Sub-optimum coherent radar detection in a mixture of K-distributed and Gaussian clutter[J]. IEE Proceedings- Radar, Sonar and Navigation, 1997, 144(1): 39-48.
    [10] Reed I S, Mallett J D, and Brennan L E. Rapid convergence rate in adaptive arrays[J]. IEEE Transactions on Aerospace and Electronic Systems, 1974, 10(6): 853-863.
    [11] Melvin W L and Showman G A. An approach to knowledge-aided covariance estimation[J]. IEEE Transactions on Aerospace and Electronic Systems, 2006, 42(3): 1021-1042.
    [12] Melvin W L. Space-time adaptive radar performance in heterogeneous clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2000, 36(2): 621-633.
    [13] Richmond C D. Performance of a class of adaptive detection algorithms in nonhomogeneous environments[J]. IEEE Transactions on Signal Processing, 2000, 48(5): 1248-1262.
    [14] Maio A D, Nicola S D, Landi L, et al.. Knowledge-aided covariance matrix estimation: a MAXDET approach[J]. IET Radar, Sonar Navigation, 2009, 3(4): 341-356.
    [15] Besson O, Tourneret J Y, and Bidon S. Knowledge-aided Bayesian detection in heterogeneous environments[J]. IEEE Signal Processing Letters, 2007, 14(5): 355-358.
    [16] Bidon S, Besson O, and Tourneret J Y. A Bayesian approach to adaptive detection in nonhomogeneous environments[J]. IEEE Transactions on Signal Processing, 2008, 56(1): 205-217.
    [17] Rabideau D J and Steinhardt A O. Improved adaptive clutter cancellation through data-adaptive training[J]. IEEE Transactions on Aerospace and Electronic Systems, 1999, 35(3): 879-891.
    [18] Nitzberg R. Application of maximum likelihood estimation of persymmetric covariance matrices to adaptive processing[J]. IEEE Transactions on Aerospace and Electronic Systems, 1980, 16(1): 124-127.
    [19] Li H, Stoica P, and Li J. Computationally efficient maximum likelihood estimation of structured covariance matrices[J]. IEEE Transactions on Signal Processing, 1999, 47(5): 1314-1323.
    [20] Roman J R, Rangaswamy M, Davis D W, et al.. Parametric adaptive matched filter for airborne radar applications[J]. IEEE Transactions on Aerospace and Electronic Systems, 2000, 36(2): 677-692.
    [21] Alfano G, Maio A D, and Farina A. Model-based adaptive detection of range-spread targets[J]. IEE Proceedings- Radar, Sonar and Navigation, 2004, 151(1): 2-10.
    [22] Wang P, Li H B, and Himed B. A parametric moving target detector for distributed MIMO radar in non-homogeneous environment[J]. IEEE Transactions on Signal Processing, 2013, 61(9): 2282-2294.
    [23] Cai L and Wang H. A persymmetric multiband GLR algorithm[J]. IEEE Transactions on Aerospace and Electronic Systems, 1992, 28(3): 806-816.
    [24] Casillo M, Maio A D, Iommelli S, et al.. A persymmetric GLRT for adaptive detection in partially-homogeneous environment[J]. IEEE Signal Processing Letters, 2007, 14(12): 1016-1019.
    [25] Hao C P, Orlando D, Ma X C, et al.. Persymmetric Rao and Wald tests for partially homogeneous environment[J]. IEEE Signal Processing Letters, 2012, 19(9): 587-590.
    [26] Pailloux G, Forster P, Ovarlez J P, et al.. Persymmetric adaptive radar detectors[J]. IEEE Transactions on Aerospace and Electronic Systems, 2011, 39(2): 2376-2390.
    [27] Gao Y C, Liao G S, Zhu S Q, et al.. A persymmetric GLRT for adaptive detection in compound-Gaussian clutter with random texture[J]. IEEE Signal Processing Letters, 2013, 20(6): 615-618.
    [28] Gao Y C, Liao G S, Zhu S Q, et al.. Persymmetric adaptive detectors in homogeneous and partially homogeneous environments[J]. IEEE Transactions on Signal Processing, 2014, 62(2): 331-342.
    [29] Hao C, Orlando D, Ma X, et al.. Persymmetric detectors with enhanced rejection capabilities[J]. IET Radar, Sonar
    [30] Navigation, 2014, 8(5): 557-563. Hao C, Orlando D, Foglia G, et al.. Persymmetric adaptive detection of distributed targets in partially-homogeneous environment[J]. Digital Signal Processing, 2014, 24: 42-51.
    [31] Wang P, Sahinoglu Z, Pun M, et al.. Persymmetric parametric adaptive matched filter for multichannel adaptive signal detection[J]. IEEE Transactions on Signal Processing, 2012, 60(6): 3322-3328.
    [32] Conte E and Maio A D. Exploiting persymmetry for CFAR detection in compound-Gaussian clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2003, 39(2): 719-724.
    [33] Conte E and Maio A D. Mitigation techniques for non-Gaussian sea clutter[J]. IEEE Journal of Ocean Engineering, 2004, 29(2): 284-302.
    [34] Maio A D, Foglia G, Conte E, et al.. CFAR behavior of adaptive detectors: an experimental analysis[J]. IEEE Transactions on Aerospace and Electronic Systems, 2005, 41(1): 233-251.
    [35] Javier C M, Javier G M, Alberto A L, et al.. Small-target detection in high-resolution heterogeneous sea-clutter: an empirical analysis[J]. IEEE Transactions on Aerospace and Electronic Systems, 2011, 47(3): 1880-1898.
    [36] Bon N, Khenchaf A, and Garello R. GLRT subspace detection for range and Doppler distributed targets[J]. IEEE Transactions on Aerospace and Electronic Systems, 2008, 44(2): 678-696.
    [37] Jin Y and Friedlander B. A CFAR adaptive subspace detector for second-order Gaussian signals[J]. IEEE Transactions on Signal Processing, 2005, 53(3): 871-884.
    [38] Conte E, Maio A D, and Ricci G. GLRT-based adaptive detection algorithms for range-spread targets[J]. IEEE Transactions on Signal Processing, 2001, 49(7): 1336-1348.
    [39] Raghavan R S, Pulsone N, and McLaughlin D J. Performance of the GLRT for adaptive vector subspace detection[J]. IEEE Transactions on Aerospace and Electronic Systems, 1996, 32(4): 1473-1487.
    [40] Kelly E J and Forsythe K M. Adaptive detection and parameter estimation for multidimensional signal models[R]. Lincoln Lab., Mass. Inst. Technol., Lexington, Tech. Rep. 848, 1989.
    [41] Maio A D and Ricci G. A polarimetric adaptive matched filter[J]. Signal Processing, 2001, 81(12): 2583-2589.
    [42] Gini F and Greco M. Covariance matrix estimation for CFAR detection in correlated heavy tailed clutter[J]. Signal Processing, 2002, 82: 1847-1859.
    [43] Kelly E J. Performance of an adaptive detection algorithm rejection of unwanted signals[J]. IEEE Transactions on Aerospace and Electronic Systems, 1989, 25(2): 122-133.
  • [1] 丁昊王国庆刘宁波关键 . 逆Gamma纹理背景下两类子空间目标的自适应检测方法. 雷达学报, 2017, 6(3): 275-284. doi: 10.12000/JR16088
    [2] 韩金旺张子敬刘军赵永波 . 基于贝叶斯的高斯杂波背景下MIMO雷达自适应检测算法. 雷达学报, 2019, 8(4): 501-509. doi: 10.12000/JR18090
    [3] 周明马亮王宁杨予昊 . 面向海面目标检测的陆海分离和海面分区算法研究. 雷达学报, 2019, 8(3): 366-372. doi: 10.12000/JR19036
    [4] 邹鲲吴德伟张斌李伟 . 一种针对失配信号的可调自适应检测器. 雷达学报, 2015, 4(4): 411-417. doi: 10.12000/JR14129
    [5] 赵军香梁兴东李焱磊 . 一种基于似然比统计量的SAR相干变化检测. 雷达学报, 2017, 6(2): 186-194. doi: 10.12000/JR16065
    [6] 许述文石星宇水鹏朗 . 复合高斯杂波下抑制失配信号的自适应检测器. 雷达学报, 2019, 8(3): 326-334. doi: 10.12000/JR19030
    [7] 李海刘新龙周盟刘维建 . 基于修正自适应匹配滤波器的机动目标检测方法. 雷达学报, 2015, 4(5): 552-559. doi: 10.12000/JR15105
    [8] 黄瑞杜小勇胡卫东 . OFDM雷达多目标运动参数的近似最大似然估计. 雷达学报, 2018, 7(4): 507-513. doi: 10.12000/JR17116
    [9] 刘维建王利才狄源水简涛谢谠王永良 . 自适应能量检测器及在失配信号检测中的应用(英文). 雷达学报, 2015, 4(2): 149-159. doi: 10.12000/JR14132
    [10] 覃尧黄春琳陆珉徐伟 . 基于小波变换与主成分分析的探地雷达自适应杂波抑制方法研究. 雷达学报, 2015, 4(4): 445-451. doi: 10.12000/JR15013
    [11] 陈帅霖罗丰张林让胡冲陈世超 . 基于动态规划的机动目标加权自适应相参积累方法. 雷达学报, 2017, 6(3): 309-315. doi: 10.12000/JR17002
    [12] 钱广华李颖骆荣剑 . 机动目标跟踪中一种机动频率和方差自适应滤波算法. 雷达学报, 2013, 2(2): 257-264. doi: 10.3724/SP.J.1300.2013.13003
    [13] 王永良刘维建谢文冲段克清高飞王泽涛 . 机载雷达空时自适应检测方法研究进展. 雷达学报, 2014, 3(2): 201-207. doi: 10.3724/SP.J.1300.2014.13081
    [14] 张林赵志坚关键何友 . 基于自适应阈值选择的非参量GS 检测算法. 雷达学报, 2012, 1(4): 387-392. doi: 10.3724/SP.J.1300.2012.20084
    [15] 周豪胡国平汪云 . 基于自适应步长萤火虫-多重信号分类算法的低空目标波达方向估计. 雷达学报, 2015, 4(3): 309-316. doi: 10.12000/JR14142
    [16] 吴孙勇薛秋条朱圣棋闫青竹孙希延 . 杂波环境下基于粒子滤波的微弱扩展目标检测前跟踪算法. 雷达学报, 2017, 6(3): 252-258. doi: 10.12000/JR16128
    [17] 张丹丹仇晓兰胡东辉丁赤飚 . 基于运动目标检测的同步轨道星-空双站SAR 杂波特性分析. 雷达学报, 2013, 2(3): 348-356. doi: 10.3724/SP.J.1300.2013.13006
    [18] 卜运成王宇张福博冀广宇陈龙永梁兴东 . 基于子空间正交的阵列干涉SAR系统相位中心位置定标方法. 雷达学报, 2018, 7(3): 335-345. doi: 10.12000/JR18007
    [19] 周春晖李飞李宁郑慧芳王翔宇 . 改进的基于特征子空间的SAR图像射频干扰抑制算法. 雷达学报, 2018, 7(2): 235-243. doi: 10.12000/JR17025
    [20] 陈希信 . 天波雷达后多普勒自适应波束形成. 雷达学报, 2016, 5(4): 373-377. doi: 10.12000/JR15124
  • 加载中
计量
  • 文章访问数:  917
  • HTML浏览量:  164
  • PDF下载量:  1147
  • 被引次数: 0
出版历程
  • 收稿日期:  2014-11-17
  • 录用日期:  2015-01-04
  • 刊出日期:  2015-08-28

均匀和部分均匀杂波中子空间目标的斜对称自适应检测方法

    通讯作者: 丁昊, hao3431@tom.com
    作者简介: 丁昊(1988-),男,博士研究生,主要研究方向为海杂波特性认知、雷达目标检测等。E-mail:hao3431@tom.com;薛永华(1985-),男,博士研究生,主要研究方向为MIMO天波雷达电离层信道仿真、海杂波仿真、波形设计、目标检测等。E-mail:xueyhchina@163.com;黄勇(1979-),男,博士,主要研究方向为雷达目标检测、海杂波特性分析等。E-mail:huangyong_2003@163.com;关键(1968-),男,教授,博士生导师,主要研究方向为雷达目标检测与跟踪、侦察图像处理和信息融合。获全国优秀博士学位论文奖,新世纪百千万人才工程国家级人选。E-mail:guanjian96@tsinghua.org.cn
  • 1. (海军航空工程学院电子信息工程系 烟台 264001)
基金项目:  国家自然科学基金(61179017, 61201445)和国家自然科学基金青年科学基金(61401495)资助课题

摘要: 在雷达目标的自适应检测领域, 当参考单元数不足时, 充分挖掘协方差矩阵的结构信息是有效提高检测性能的途径之一。为此, 针对多维子空间目标的检测问题, 该文在协方差矩阵关于次对角线具有斜对称结构的约束下, 分别基于一步和两步广义似然比(GLRT), 推导了均匀和部分均匀杂波中的斜对称自适应检测器。由于检测器在设计阶段利用了协方差矩阵的结构信息, 仿真结果表明, 与已有检测器相比, 在参考单元数不足时, 斜对称自适应检测器可明显改善检测性能。此外, 分别从协方差估计方法的影响、目标子空间维数的影响、目标子空间失配性能以及目标起伏的影响4个方面对检测性能进行了仿真分析。

English Abstract

参考文献 (43)

目录

    /

    返回文章
    返回