海杂波背景下的双极化最大特征值目标检测

doi:10.12305/j.issn.1001-506X.2024.11.13

摘要/Abstract

摘要：

现有的矩阵恒虚警率(constant false alarm rate, CFAR)检测器仅对单一极化回波进行处理，且计算复杂度较高，导致相关性信息使用不充分、检测场景受限。对此，利用双极化雷达回波计算协方差矩阵，通过提取其特征值来衡量回波间的关联性，提出一种计算复杂度较低的基于双极化最大特征值的检测方法。首先，推导了以最大特征值作为几何距离进行检测的可行性。进一步，设计了可在实际场景中使用的基于双极化最大特征值的矩阵CFAR检测器。最后，通过实测海杂波数据验证了所提方法的实际检测能力。仿真结果表明，所提方法相比同样使用最大特征值作为检测统计量的最大特征值矩阵测度算法具有更好的杂波抑制效果，检测性能也优于非相参积累的单元平均CFAR检测方法。

关键词: 海杂波, 目标检测, 特征值, 双极化

Abstract:

Due to the existing matrix constant false alarm rate (CFAR) detectors only processing single polarized echoes and their high computational complexity, the use of correlation information is insufficient and the detection scenario is limited. In this regard, a detection method based on the maximum eigenvalue of dual polarization is proposed, which utilizes the covariance matrix of dual polarization radar echoes to measure the correlation between echoes by extracting their eigenvalues and has lower computational complexity. Firstly, the feasibility of using the maximum eigenvalue as the geometric distance for detection is derived. Furthermore, a matrix CFAR detector based on dual polarization maximum eigenvalue is designed for practical use in real-world scenarios. Finally, the actual detection capability of the proposed method is verified through measured sea clutter data. Simulation results show that the proposed method has better clutter suppression performance and detection performance compared to the maximum eigenvalue matrix measurement algorithm, which also uses the maximum eigenvalue as the detection statistic. The detection performance is also better than the non coherent accumulation cell-averaging CFAR detection method.

Key words: sea clutter, target detection, eigenvalue, dual polarization

中图分类号:

TN957.51

关键, 姜星宇, 刘宁波, 丁昊, 黄勇. 海杂波背景下的双极化最大特征值目标检测[J]. 系统工程与电子技术, 2024, 46(11): 3715-3725.

Jian GUAN, Xingyu JIANG, Ningbo LIU, Hao DING, Yong HUANG. Dual-polarized maximum eigenvalue-based target detection in sea clutter environment[J]. Systems Engineering and Electronics, 2024, 46(11): 3715-3725.

图/表 17

图1

表1

图2

表2

图3

图4

图5

图6

图7

图8

图9

图10

图11

图12

图13

图14

图15

参考文献 31

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.
	DING H , DONG Y L , LIU N B , et al. Research progress and prospect of sea clutter characteristic cognition[J]. Journal of Radars, 2016, 5 (5): 499- 516.
2	丁昊, 刘宁波, 董云龙, 等. 雷达海杂波测量试验回顾与展望[J]. 雷达学报, 2019, 8 (3): 281- 302.
	DING H , LIU N B , DONG Y L , et al. Overview and prospects of radar sea clutter measurement experiments[J]. Journal of Radars, 2019, 8 (3): 281- 302.
3	ARMSTRONG B C , GRIFFITHS H D . CFAR detection of fluctuating targets in spatially correlated K-distributed clutter[J]. IEE Proceedings. Part F (Radar and Signal Processing), 1991, 138 (2): 139- 152. doi: 10.1049/ip-f-2.1991.0020
4	SEPULCHRE R , ABSIL P A , MAHONY R . Optimization algorithms on matrix manifolds[M]. Princeton: Princeton University Press, 2008.
5	BARBARESCO F. Innovative tools for radar signal processing based on Cartan's geometry of SPD matrices & information geometry[C]//Proc. of the IEEE Radar Conference, 2008: 1370-1375.
6	BARBARESCO F. New foundation of radar Doppler signal processing based on advanced differential geometry of symmetric spaces: Doppler matrix CFAR and radar application[C]//Proc. of the International Radar Conference, 2009: 110-116.
7	ARNAUDON M , BARBARESCO F , YANG L . Riemannian medians and means with applications to radar signal processing[J]. IEEE Journal of Selected Topics in Signal Processing, 2013, 7 (4): 595- 604. doi: 10.1109/JSTSP.2013.2261798
8	DECURNINGE A , BARBARESCO F . Robust Burg estimation of radar scatter matrix for autoregressive structured SIRV based on Fréchet medians[J]. IET Radar, Sonar & Navigation, 2017, 11 (1): 78- 89.
9	华小强. 基于矩阵信息几何的雷达目标检测方法研究[D]. 长沙: 国防科学技术大学, 2018.
	HUA X Q. Research on radar target detection method based on matrix information geometry[D]. Changsha: National University of Defense Technology, 2018.
10	CHENG Y Q , HUA X Q , WANG H Q , et al. The geometry of signal detection with applications to radar signal processing[J]. Entropy, 2016, 18 (11): 381- 398. doi: 10.3390/e18110381
11	ZHAO X G , WANG S Y . An improved matrix CFAR detection method base on KL divergence[J]. Jouranal of Eletronics & Information Technology, 2016, 38 (4): 934- 940.
12	BESSON O , SCHARF L , VINCENT F . Matched direction detectors and estimators for array processing with subspace steering vector uncertainties[J]. IEEE Trans. on Signal Processing, 2005, 52 (12): 4453- 4463.
13	BESSON O , KRAUT S , SCHARF L . Detection of an unknown rank-one component in white noise[J]. IEEE Trans. on Signal Processing, 2006, 54 (7): 2835- 2839. doi: 10.1109/TSP.2006.874781
14	ZHAO W J , CHANG L . Maximum eigenvalue-based target detection for the K-distributed clutter environment[J]. IET Radar, Sonar & Navigation, 2018, 12 (11): 1294- 1306.
15	孙国皓. 基于协方差矩阵结构特征的机载雷达空时处理[D]. 成都: 电子科技大学, 2005.
	SUN G H. Airborne radar space-time processing based on structural characteristics of covariance matrix[D]. Chengdu: University of Electronic Science and Technology of China, 2019.
16	WATTS S . Modeling and simulation of coherent sea clutter[J]. IEEE Trans. on Aerospace and Electronic Systems, 2012, 48 (4): 3303- 3317.
17	GINI F , FARINA A . Vector subspace detection in compound Gaussian clutter. Part Ⅰ: survey and new results[J]. IEEE Trans. on Aerospace and Elctronic System, 2002, 38 (4): 1295- 1311.
18	GINI F , FARINA A , MONTANARIM M . Vector subspace detection in compound Gaussian clutter. Part Ⅱ: performance analysis[J]. IEEE Trans. on Aerospace and Elctronic System, 2002, 38 (4): 1312- 1323.
19	TULINO A M , VERDU S . Random matrix theory and wireless communications[J]. Foundations and Trends in Communications and Information Theory, 2004, 1 (1): 1- 182.
20	HUA X Q , CHENG Y Q , WANG H Q , et al. Geometric means and medians with applications to target detection[J]. IET Signal Processing, 2017, 11 (6): 711- 720.
21	HUA X Q , CHENG Y Q , LI Y B , et al. Target detection in sea clutter via weighted averaging filter on the Riemanian[J]. Aerospace Science and Technology, 2017, 70, 47- 54.
22	HUA X Q , CHENG Y Q , WANG H Q , et al. Matrix CFAR detectors based on symmetrized Kullback-Leibler and total Kullback-Leibler divergences[J]. Digital Signal Processing, 2017, 69, 106- 116.
23	DE-MAIO A , ORLANDO D , SOLOVEYCHIK I . Invariance theory for adaption detection in interference with group symmetric covariance matrix[J]. IEEE Trans. on Signal Processing, 2016, 64 (23): 6299- 6312.
24	RATNARAJAH T , VAILLANCOURT R , ALVO M . Eigenvalues and condition numbers of complex random matrices[J]. SIAM Journal on Matrix Analysis and Applications, 2004, 26 (2): 441- 456.
25	张贤达. 矩阵分析与应用[M]. 2版北京: 清华大学出版社, 2013: 285- 323.
	ZHANG X D . Matrix analysis and applications[M]. 2nd ed Beijing: Press of Tsinghua University, 2013: 285- 323.
26	ZHAO W J , JIN M L . Maximum eigenvalue matrix CFAR detection using pre-processing in sea clutter[J]. IEEE Access, 2019, 7, 91414- 91426.
27	赵文静, 刘文龙, 金明录. 海杂波环境下改进的中值矩阵检测方法[J]. 系统工程与电子技术, 2018, 40 (10): 2173- 2179.
	ZHAO W J , LIU W L , JIN M L . Modified median matrix detection method for sea clutter environment[J]. Systems Engineering and Electronics, 2018, 40 (10): 2173- 2179.
28	ZHAO W J , CHANG L , LIU W L . Spectral norm based mean matrix estimation and its application to radar target CFAR detection[J]. IEEE Trans. on Signal Processing, 2019, 67 (22): 5746- 5760.
29	刘宁波, 董云龙, 王国庆, 等. X波段雷达对海探测试验与数据获取[J]. 雷达学报, 2019, 8 (5): 656- 667.
	LIU N B , DONG Y L , WANG G Q , et al. Sea-detecting X-band radar and data acquisition program[J]. Journal of Radars, 2019, 8 (5): 656- 667.
30	刘宁波, 董云龙, 王国庆, 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.
	LIU N B , DONG Y L , WANG G Q , et al. Annual progress of the sea-detecting X-band radar and data acquisition program[J]. Journal of Radars, 2021, 10 (1): 173- 182.
31	关键, 刘宁波, 王国庆, 等. 雷达对海探测试验与目标特性数据获取——海上目标双极化多海况散射特性数据集[J]. 雷达学报, 2023, 12 (2): 456- 469.
	GUAN J , LIU N B , WANG G Q , et al. Sea-detecting radar experiment and target feature data acquisition for dual polarization multistate scattering dataset of marine targets[J]. Journal of Radars, 2023, 12 (2): 456- 469.

方法	几何距离	均值矩阵
MEMD	$d_{\operatorname{MEMD}}\left(\lambda_{\max }^{(i)}, \lambda_{\max }^{\mathrm{D}}\right)=\frac{\lambda_{\max }^{\mathrm{D}}}{\frac{1}{N} \sum\limits_{i=1}^N \lambda_{\max }^{(i)}}$	-
LE	$\begin{gathered}d_{\mathrm{LE}}\left(\hat{\boldsymbol{R}}, \boldsymbol{R}_{\mathrm{D}}\right)= \\\left\\|\log _2(\hat{\boldsymbol{R}})-\log _2\left(\boldsymbol{R}_{\mathrm{D}}\right)\right\\|_{\mathrm{F}}\end{gathered}$	$\hat{\boldsymbol{R}}=\exp \left(\frac{1}{N} \sum\limits_{i=1}^N \log _2\left(\boldsymbol{R}_i\right)\right)$
LD	$\begin{gathered}d_{\mathrm{LD}}\left(\hat{\boldsymbol{R}}, \boldsymbol{R}_{\mathrm{D}}\right)= \\\operatorname{tr}\left(\boldsymbol{R}_{\mathrm{D}}^{-1}\left(\hat{\boldsymbol{R}} \boldsymbol{R}_{\mathrm{D}}\right)\right)-\log _2 \operatorname{det}\left(\hat{\boldsymbol{R}} \boldsymbol{R}_{\mathrm{D}}^{-1}\right)\end{gathered}$	$\hat{\boldsymbol{R}}=\left(\frac{1}{N} \sum\limits_{i=1}^N \boldsymbol{R}_i^{-1}\right)^{-1}$

方法	计算复杂度
DMED	(N+1)M³+N
MEMD	(N+1)M³+N
LE	M!+(N+3)M³+(N+1)M²+M
LD	M³+(N+2)M²+2M－1

[1]	吴巍, 薛冰, 刘丹丹. 基于Tri-feature训练的目标与海杂波鉴别算法[J]. 系统工程与电子技术, 2024, 46(9): 2935-2940.
[2]	蔡伟, 王鑫, 蒋昕昊, 杨志勇, 陈栋. 基于解耦的小样本目标检测方法研究[J]. 系统工程与电子技术, 2024, 46(9): 2941-2950.
[3]	颜上取, 付耀文, 张文鹏, 杨威, 余若峰, 张法桐. 视频合成孔径雷达技术发展现状综述[J]. 系统工程与电子技术, 2024, 46(8): 2650-2666.
[4]	连静, 杨勇, 谢晓霞, 王雪松. 大掠射角对海雷达导引头实测回波特性分析[J]. 系统工程与电子技术, 2024, 46(5): 1535-1543.
[5]	李晓辉, 万宏杰, 石明利, 王先文. 基于特征值的动态数字信道化子带检测算法[J]. 系统工程与电子技术, 2024, 46(5): 1801-1809.
[6]	周利, 胡杰民, 付连庆, 凌三力. 基于MDCFT与水平集的高海情弹载雷达成像检测方法[J]. 系统工程与电子技术, 2024, 46(4): 1247-1254.
[7]	黄思佳, 宋纯锋, 李璇. 基于可变尺度先验框的声呐图像目标检测[J]. 系统工程与电子技术, 2024, 46(3): 771-778.
[8]	扈琪, 胡绍海, 刘帅奇. 基于多层显著性模型的SAR图像舰船目标检测[J]. 系统工程与电子技术, 2024, 46(2): 478-487.
[9]	宋存利, 柴伟琴, 张雪松. 基于改进YOLO v5算法的道路小目标检测[J]. 系统工程与电子技术, 2024, 46(10): 3271-3278.
[10]	黄广佳, 程旭, 饶彬, 王伟. 基于广义Rao检验的单/多比特MIMO雷达运动目标检测方法[J]. 系统工程与电子技术, 2024, 46(1): 105-112.
[11]	施端阳, 林强, 胡冰, 杜小帅. 基于YOLO的航管一次雷达目标检测方法[J]. 系统工程与电子技术, 2024, 46(1): 143-151.
[12]	汪萌, 诸兵. 不确定性建模在2D和3D目标检测中的应用[J]. 系统工程与电子技术, 2023, 45(8): 2370-2376.
[13]	成倩, 李佳, 杜娟. 基于YOLOv5的光学遥感图像舰船目标检测算法[J]. 系统工程与电子技术, 2023, 45(5): 1270-1276.
[14]	朱晶晶, 朱圣棋, 廖桂生, 许京伟, 兰岚, 曾操. 相控阵和频率分集阵双模式雷达联合目标检测[J]. 系统工程与电子技术, 2023, 45(5): 1342-1350.
[15]	杨宇超, 方明, 赵晨帆, 方刚. 高速机动目标长时间相参积累算法[J]. 系统工程与电子技术, 2023, 45(5): 1359-1370.