基于逆高斯纹理分布的协方差矩阵估计方法

doi:10.12305/j.issn.1001-506X.2021.09.13

系统工程与电子技术 ›› 2021, Vol. 43 ›› Issue (9): 2470-2475.doi: 10.12305/j.issn.1001-506X.2021.09.13

基于逆高斯纹理分布的协方差矩阵估计方法

李锐洋^1,*, 霍伟博², 马巍¹, 程子扬²

1. 中国电子科技集团公司第二十九研究所, 四川成都 610036
2. 电子科技大学信息与通信工程学院, 四川成都 611731

收稿日期:2020-10-21 出版日期:2021-08-20 发布日期:2021-08-26
通讯作者: 李锐洋
作者简介:李锐洋(1988—), 男, 高级工程师, 博士, 主要研究方向为雷达空时信号处理、雷达侦察识别|霍伟博(1987—), 男, 博士后, 主要研究方向为海杂波建模、雷达目标检测、信号分析|马巍(1985—), 男, 高级工程师, 博士, 主要研究方向为阵列信号处理、目标智能化识别|程子杨(1990—), 男, 副研究员, 博士, 主要研究方向为MIMO雷达、相控阵雷达、雷达波形设计
基金资助:
国家自然科学基金(62001084)

Covariance matrix estimation method based on inverse Gaussian texture distribution

Ruiyang LI^1,*, Weibo HUO², Wei MA¹, Ziyang CHENG²

1. Southwest China Research Institute of Electronic Equipment, Chengdu 610036, China)
2. School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

Received:2020-10-21 Online:2021-08-20 Published:2021-08-26
Contact: Ruiyang LI

摘要/Abstract

摘要：

在复合高斯杂波中检测目标信号, 需要对杂波协方差矩阵进行估计, 相应的检测性能与估计精度密切相关。利用服从逆高斯分布的纹理分量来对复合高斯杂波进行建模, 可以更好地拟合高分辨杂波实测数据。本文给出了一种两步广义似然比检测器, 先假设杂波协方差矩阵已知以获得检测统计量, 再利用纹理分量的先验分布推导协方差矩阵的最大似然估计。同时，基于贝叶斯方法, 假定纹理分量和协方差矩阵均为服从某种先验分布的随机量, 推导了协方差矩阵的最大后验估计。仿真结果显示, 基于知识的自适应检测技术由于引入了纹理分量和杂波的先验信息, 其协方差矩阵的估计精度好于最大似然估计和样本估计方法, 同时具有更好的检测性能。

关键词: 信号检测, 非均匀杂波, 知识辅助, 逆高斯分布

Abstract:

To detect the target signal in composite Gaussian clutter, the clutter covariance matrix needs to be estimated. The corresponding detection performance is closely related to the estimation accuracy. Using the texture component obeying the inverse Gaussian distribution to model the composite Gaussian clutter can better fit the measured data of high-resolution clutter. In this paper, a two-step generalized likelihood ratio detector is proposed. Firstly, the clutter covariance matrix is assumed to be known to obtain the detection statistics, and then the maximum likelihood estimation of the covariance matrix is derived from the prior distribution of texture components. At the same time, based on Bayesian method, assuming that the texture component and covariance matrix are random quantities subject to a priori distribution, the maximum a posteriori estimation of covariance matrix is derived. Simulation results show that due to the introduction of prior information of texture components and clutter, the estimation accuracy of covariance matrix of knowledge-based adaptive detection technology is better than that of maximum likelihood estimation and sample estimation methods, and has better detection performance.

Key words: signal detection, nonhomogeneous clutter, knowledge aided, inverse Gaussian distribution

中图分类号:

TN957.51

李锐洋, 霍伟博, 马巍, 程子扬. 基于逆高斯纹理分布的协方差矩阵估计方法[J]. 系统工程与电子技术, 2021, 43(9): 2470-2475.

Ruiyang LI, Weibo HUO, Wei MA, Ziyang CHENG. Covariance matrix estimation method based on inverse Gaussian texture distribution[J]. Systems Engineering and Electronics, 2021, 43(9): 2470-2475.

图/表 2

参考文献 21

1	KELLY E J . An adaptive detection algorithm[J]. IEEE Trans.on Aerospace and Electronic Systems, 1986, 22 (1): 115- 127.
2	CARRETERO-MOYA J , GISMERO-MENOYO J , BLANCO-DEL-CAMPO Á , et al. Statistical analysis of a high-resolution sea-clutter database[J]. IEEE Trans.on Geoscience and Remote Sensing, 2010, 48 (4): 2024- 2037. doi: 10.1109/TGRS.2009.2033193
3	BILLINGSLEY J B , FARINA A , GINI F , et al. Statistical analyses of measured radar ground clutter data[J]. IEEE Trans.on Aerospace and Electronic Systems, 1999, 35 (2): 579- 593. doi: 10.1109/7.766939
4	任双娇, 刘永祥, 黎湘, 等. 广义K分布杂波模型参数估计[J]. 电子学报, 2006, 34 (12): 2278- 2281. doi: 10.3321/j.issn:0372-2112.2006.12.033
	REN S J , LIU Y X , LI X , et al. Parameters estimation for generalized K-distributed clutter model[J]. Acta Electronica Sinica, 2006, 34 (12): 2278- 2281. doi: 10.3321/j.issn:0372-2112.2006.12.033
5	CONTE E , DE M A , GALDI C . Statistical analysis of real clutter at different range resolutions[J]. IEEE Trans.on Aerospace and Electronic Systems, 2004, 40 (3): 903- 918. doi: 10.1109/TAES.2004.1337463
6	姜斌, 黎湘, 王宏强, 等. 近海岸复合杂波建模及其统计特性研究[J]. 系统工程与电子技术, 2006, 28 (2): 221- 223, 309. doi: 10.3321/j.issn:1001-506X.2006.02.017
	JIANG B , LI X , WANG H Q , et al. Studies on the modeling and statistic characteristic of compound clutter under the coastal environment[J]. Systems Engineering and Electronics, 2006, 28 (2): 221- 223, 309. doi: 10.3321/j.issn:1001-506X.2006.02.017
7	CONTE E , MAIO A D . Mitigation techniques for non-Gaussian sea clutter[J]. IEEE Journal of Oceanic Engineering, 2004, 29 (2): 284- 302. doi: 10.1109/JOE.2004.826901
8	BALLERI A , NEHORAI A , WANG J . Maximum likelihood estimation for compound-Gaussian clutter with inverse Gamma texture[J]. IEEE Trans.on Aerospace and Electronic Systems, 2007, 43 (2): 775- 779. doi: 10.1109/TAES.2007.4285370
9	SHANG X , SONG H . Radar detection based on compound-Gaussian model with inverse Gamma texture[J]. IET Radar, Sonar & Navigation, 2011, 5 (3): 315- 321.
10	CONTE E , LOPS M , RICCI G . Adaptive matched filter detection in spherically invariant noise[J]. IEEE Signal Processing Letters, 1996, 3 (8): 248- 250. doi: 10.1109/97.511809
11	GINI F , 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
12	CHITOUR Y , PASCAL F . Exact maximum likelihood estimates for SIRV covariance matrix: existence and algorithm analysis[J]. IEEE Trans.on Signal Processing, 2008, 56 (10): 4563- 4573. doi: 10.1109/TSP.2008.927464
13	GINI F , RANGASWAMY M . Knowledge based radar detection, tracking and classification[M]. New Jersey: John Wiley and Sons, 2008.
14	GAO Y , LI H , HIMED B . Knowledge-aided range-spread target detection for distributed MIMO radar in nonhomogeneous environments[J]. IEEE Trans.on Signal Processing, 2017, 65 (3): 617- 627. doi: 10.1109/TSP.2016.2625266
15	OLLILA E , TYLER D E , KOIVUNEN V , et al. Compound-Gaussian clutter modeling with an inverse Gaussian texture distribution[J]. IEEE Signal Processing Letters, 2012, 19 (12): 876- 879. doi: 10.1109/LSP.2012.2221698
16	SHUI P L , SHI L X , YU H , 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
17	张洋忠, 张玉, 胡进, 等. 复合高斯杂波中知识辅助的自适应检测算法[J]. 现代雷达, 2016, 38 (7): 40- 43.
	ZHANG Y Z , ZHANG Y , HU J , et al. Knowledge-aided adaptive detection algorithm in compound-Gaussian clutter[J]. Modern Radar, 2016, 38 (7): 40- 43.
18	时艳玲, 刘浩, 刘子鹏, 等. 基于GLRT的逆高斯纹理海杂波背景下目标检测器[J]. 信号处理, 2020, 36 (12): 2067- 2073.
	SHI Y L , LIU H , LIU Z P , et al. The GLRT to detect target in sea clutter with inverse Gaussian texture[J]. Journal of Signal Processing, 2020, 36 (12): 2067- 2073.
19	XU S W , XUE J , SHUI P L . Adaptive detection of range-spread targets in compound Gaussian clutter with the square root of inverse Gaussian texture[J]. Digital Signal Processing, 2016, 56, 132- 139. doi: 10.1016/j.dsp.2016.06.009
20	施赛楠, 水鹏朗, 杨春娇, 等. 基于逆高斯纹理空间相关性的雷达目标检测[J]. 系统工程与电子技术, 2017, 39 (10): 2215- 2220.
	SHI S N , SHUI P L , YANG C J , et al. Radar target detection based on spatial correlation of inverse Gaussian texture[J]. Systems Engineering and Electronics, 2017, 39 (10): 2215- 2220.
21	GRADSHTEYN I S , RYZHIK I M . Table of integrals, series, and products[J]. Mathematics of Computation, 2007, 20 (96): 1157- 1160.

[1]	梁璞, 范红旗, 付强. 基于投影余角的多普勒雷达几何检测器[J]. 系统工程与电子技术, 2022, 44(6): 1854-1861.
[2]	刘步花, 丁丹, 杨柳, 薛乃阳, 刘仲谦. 基于DNN的无人机数据OFDM传输技术[J]. 系统工程与电子技术, 2022, 44(2): 696-702.
[3]	许耀华, 朱成龙, 王翊, 蒋芳, 丁梦琴, 王慧平. 基于神经网络的高并行大规模MIMO信号检测算法[J]. 系统工程与电子技术, 2022, 44(12): 3843-3849.
[4]	高涌荇, 王旭东, 汪玲, 朱岱寅, 郭军, 孟凡旺. 基于RCNN的双极化气象雷达天气信号检测[J]. 系统工程与电子技术, 2022, 44(11): 3380-3387.
[5]	王华华, 李延山, 余永坤. 低分辨率ADC下MIMO-OFDM系统中的广义Turbo信号检测[J]. 系统工程与电子技术, 2022, 44(1): 299-306.
[6]	王华华, 余永坤, 于敏. MU-MIMO单比特ADC系统中软输出检测算法[J]. 系统工程与电子技术, 2021, 43(2): 555-560.
[7]	梁静雯, 朱江. 时间反演系统中基于Barzilai-Borwein的共轭梯度检测算法[J]. 系统工程与电子技术, 2021, 43(2): 567-573.
[8]	凌云飞, 陈长兴, 牛德智, 陈婷. 基于Poincare截面的微弱信号定量检测与幅值参数提取[J]. 系统工程与电子技术, 2020, 42(6): 1210-1217.
[9]	肖浩, 王彤, 文才, 苏昱煜. 一种迭代可分离的机载面阵雷达杂波抑制方法[J]. 系统工程与电子技术, 2020, 42(11): 2461-2470.
[10]	代振, 王平波, 娄良轲. SαS分布下的累积分布检测器[J]. 系统工程与电子技术, 2020, 42(1): 10-14.
[11]	凌云飞, 陈长兴, 牛德智, 陈婷. 双势阱Duffing-van der Pol振子微弱信号检测统计量构造[J]. 系统工程与电子技术, 2019, 41(10): 2178-2183.
[12]	刘孝祥, 张晶. 基于Barzilai-Borwein迭代的低复杂度大规模MIMO信号检测算法[J]. 系统工程与电子技术, 2018, 40(8): 1861-1865.
[13]	曹健, 王兆祎, 胡进峰, 何子述. 基于知识辅助的天波雷达海杂波抑制方法[J]. 系统工程与电子技术, 2018, 40(3): 533-537.
[14]	刘科, 吴文启, 唐康华, 武智佳, 张施豪. 基于伪距信息的GNSS双接收机抗转发式干扰检测算法[J]. 系统工程与电子技术, 2017, 39(11): 2393-2398.
[15]	金艳, 李曙光, 姬红兵. 脉冲噪声中基于数据可信度加权的跳频信号检测[J]. 系统工程与电子技术, 2016, 38(9): 2171-2178.

基于逆高斯纹理分布的协方差矩阵估计方法

Covariance matrix estimation method based on inverse Gaussian texture distribution

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 2

参考文献 21

相关文章 15

编辑推荐

Metrics

本文评价