稀疏字典学习海面微弱动目标检测

doi:10.3969/j.issn.1001-506X.2020.01.05

Abstract

Abstract:

Aiming at the difficulty of weak moving target signals extraction and the poor radar detection performance under strong sea clutter background, a dictionary learning algorithm is proposed based on the sparse representation theory to suppress sea clutter and reconstruct the target signals. The K-singular value decomposition (K-SVD) algorithm is used in this method to learn the characteristics of principal components of sea clutter and targets in sparse domain, obtaining corresponding learning dictionaries. Based on the dictionaries, the sea clutter is first suppressed and then the target signals are reconstructed, overcoming the disadvantage of signals' low-matching degree and poor effects in signal extraction under fixed dictionaries. And then the algorithm parameters are analyzed and optimized to further promote the performance and engineering practicability. The experimental results based on the measured data show that compared with the traditional detection methods, the proposed algorithm can effectively detect the weak moving target under high sea conditions and significantly improve the detection performance.

Key words: sparse dictionary learning, sea clutter suppression, signal reconstruction, weak moving target detection

CLC Number:

TN911.23

Ziwei DONG, Jun SUN, Jingming SUN, Meiyan PAN. Marine weak moving target detection based on sparse dictionary learning[J]. Systems Engineering and Electronics, 2020, 42(1): 30-36.

Figures/Tables 13

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参数	变量值
发射频率/GHz	9
天线高度/m	67
极化方式	VV
脉冲功率/kW	2
脉冲宽度/ns	100
脉重频/kHz	5
天线模式	凝视
天线类型	双偏置反射器
天线增益/ dB	≥30
波束宽度/(°)	0.9
天线方位/(°)	166.02
距离分辨率/m	15

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Marine weak moving target detection based on sparse dictionary learning

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