基于多特征信息融合的海面微弱目标检测

doi:10.12305/j.issn.1001-506X.2022.11.07

摘要/Abstract

摘要：

为改善海杂波背景下雷达检测微弱目标的性能, 提出一种基于多特征信息融合的目标检测方法。首先, 在分析时域回波信号的基础上, 给出脉冲幅值离差比的概念, 并用其表征离散回波信号的尖锐度。其次, 结合回波信号的频率峰均比和局部分形度两种特征量, 构建多特征信息融合张量。然后, 采用交叉验证法训练支持向量机(support vectors machine, SVM)分类器, 并依据分类器进行目标检测。最后, 通过对实测海杂波数据的一系列实验分析, 优选了所提方案的参数。进一步与已有传统方法对比, 结果显示所提方法具有更好的鲁棒性。

关键词: 目标检测, 海杂波, 信息融合, 支持向量机

Abstract:

To improve the performance of radar weak target detection in sea clutter, a target detection method based on multi-feature information fusion is proposed. Firstly, on the basis of the analysis of time-domain returned signal, the pulse and amplitude deviation rate is defined to characterize the sharpness of discrete returned signal. Secondly, the multi-feature information fusion tensor is constructed by combining the frequency peak to average ratio and local grade of fractality of the returned signal. Thirdly, the support vectors machine (SVM) classifier is trained by cross validation, and the target is detected according to the classifier. Finally, by a series of experimental analysis of the measured sea clutter data, the parameters of the proposed scheme are optimized. Furthermore, the results show that the proposed method has better robustness compared with the existing traditional methods.

Key words: target detection, sea clutter, information fusion, support vector machine (SVM)

中图分类号:

TN911.23

薛春岭, 曹菲, 孙庆, 秦建强, 冯晓伟. 基于多特征信息融合的海面微弱目标检测[J]. 系统工程与电子技术, 2022, 44(11): 3338-3345.

Chunling XUE, Fei CAO, Qing SUN, Jianqiang QIN, Xiaowei FENG. Sea-surface weak target detection based on multi-feature information fusion[J]. Systems Engineering and Electronics, 2022, 44(11): 3338-3345.

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数据集	目标单元		SCR/dB	浪高/m
数据集	主目标	次目标	HH/VV/HV/VH	浪高/m
#17	9	8, 10, 11	18.3/3.7/13.0/13.0	2.1
#30	7	6, 8	-0.3/2.0/3.6/3.6	0.9
#54	8	7, 9, 10	9.5/11.0/12.9/12.8	0.7
#280	8	7, 9, 10	4.0/4.4/7.3/7.4	1.4

参数	窗口宽度
参数	512	1 024	2 048	4 096	8 192	16 384
P_d	0.615	0.606	0.602	0.703	0.688	0.688
P_fa	0.028	0.039	0.041	0.038	0.038	0.075

参数	窗口宽度
参数	32	64	128	256	512	1 024	2 048	4 096
P_d	0.808	0.807	0.802	0.706	0.780	0.701	0.709	0.703
P_fa	0.057	0.059	0.059	0.051	0.076	0.040	0.048	0.038

数据集	参数	极压模式
数据集	参数	HH	VV	HV	VH
#17	P_d	0.510	0.211	0.448	0.452
#17	P_fa	0.027	0.009	0.030	0.032
#54	P_d	0.975	0.676	0.952	0.956
#54	P_fa	0.001	0.026	0.007	0.006
#280	P_d	0.802	0.791	0.740	0.690
#280	P_fa	0.059	0.057	0.043	0.030

数据集	参数	检测方法
数据集	参数	BCD	RAA	TIE	TH-SVM	本文方法
#17	P_d	0.427	0.496	0.344	0.469	0.510
#17	P_fa	0.027	0.027	0.030	0.069	0.027
#30	P_d	0.369	0.304	0.230	0.124	0.540
#30	P_fa	0.208	0.122	0.173	0.011	0.017
#54	P_d	0.515	0.718	0.307	0.647	0.975
#54	P_fa	0.002	0.002	0.222	0.112	0.001
#280	P_d	0.138	0.291	0.354	0.341	0.802
#280	P_fa	0.710	0.062	0.454	0.057	0.059
耗时/s		0.14	0.03	0.04	0.76	0.05