基于稀疏分解的海面微动目标识别

doi:10.12305/j.issn.1001-506X.2023.04.10

摘要/Abstract

摘要：

海洋环境下杂波较强, 慢速微弱目标的多普勒频率往往会落入海杂波多普勒频宽中, 传统动目标检测方法难以检测出目标回波。为了解决此类问题, 依据海杂波与目标在震荡属性和稀疏特性上的差异, 首先利用可调Q小波变换算法分别获得对应的自适应完备字典。然后，运用形态成分分析算法得到对应的目标稀疏系数和杂波稀疏系数; 再把稀疏系数与各自的自适应字典相乘得到目标分量与杂波分量。最后, 在雷达对海探测数据集下验证了算法的有效性。

关键词: 海杂波, 目标识别, 稀疏分解, 可调Q小波变换

Abstract:

Clutter in the marine environment is strong, and the Doppler frequency of slow and weak targets often falls into the sea clutter Doppler bandwidth. It is difficult for the classic moving target detection methods to detect target echoes. In order to address such problems, this paper uses the tunable Q-factor wavelet transform (TQWT) algorithm to obtain the corresponding self-adaptive complete dictionaries according to the difference between the sea clutter and the target in the oscillation properties and sparse characteristics, and then uses the morphological component analysis (MCA) algorithm to obtain the corresponding target sparse coefficients and clutter sparse coefficients. Then, the target components and clutter components are obtained by multiplying the sparse coefficients with their respective adaptive dictionaries. Finally, the effectiveness of the algorithm is verified by using the radar sea detection dataset.

Key words: sea clutter, target recognition, sparse decomposition, tunable Q-factor wavelet transform (TQWT)

中图分类号:

TN957.54

黄瀚仪, 胡仕友, 郭胜龙, 李珊君, 舒勤. 基于稀疏分解的海面微动目标识别[J]. 系统工程与电子技术, 2023, 45(4): 1016-1023.

Hanyi HUANG, Shiyou HU, Shenglong GUO, Shanjun LI, Qin SHU. Sea surface micro-moving target recognition based on sparse decomposition[J]. Systems Engineering and Electronics, 2023, 45(4): 1016-1023.

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数据包名称	海况等级	工作模式	起始方位/(°)	脉冲个数	描述信息
20210106155330_01_staring.mat	3~4级	凝视	42.18	＞10⁴	纯海杂波
20210106155432_01_staring.mat	3~4级	凝视	42.18	＞10⁴	纯海杂波
20210106160919_02_staring.mat	3~4级	凝视	17.36	＞10⁴	2.778 km处有漂浮目标

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