基于注意力自相关机制的剩余杂波抑制方法

doi:10.19665/j.issn1001-2400.20230402

Abstract

Abstract:

Radar systems are subject to an ever-changing and complex environment that creates a non-uniform and time-varying clutter.The unsuppressed residual clutter can produce a significant number of false alarms,leading to a degraded target tracking performance,spurious trajectories creation,or saturation data processing systems,which in turn decreases the detection ability of the radar system.Conventional residual clutter suppression algorithms typically require feature extraction and classifier construction.These steps can result in poor generalization capability,difficulty in feature combination,and high requirements for the classifier.To address these issues,inspired by self-attention mechanisms and domain knowledge,this paper proposes a data- and knowledge-driven attention autocorrelation mechanism,which can effectively extract deep features of the radar echo to distinguish between targets and clutter,on the basis of which a residual clutter suppression method is constructed using the attention autocorrelation mechanism,which makes full use of the radar echo feature,thereby improving the residual clutter suppression capability.Simulation and measurement results demonstrate that this method has advantages of a significant performance and generalization capability for residual clutter suppression.Additionally,its parallel computing structure enhances the operational efficiency of the algorithm.

Key words: residual clutter suppression, attention autocorrelation mechanism, deep-features, neural networks

CLC Number:

TN957

SHEN Lu, SU Hongtao, WANG Jin, MAO Zhi, JING Xinchen, LI Ze. Attention autocorrelation mechanism-based residual clutter suppression method[J].Journal of Xidian University, 2024, 51(1): 41-51.

Figures/Tables 13

References 17

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参数	取值
雷达载频/GHz	9.39
雷达带宽/MHz	10
采样频率/MHz	80
脉冲重复周期/s	1×10^-3
脉冲宽度/s	2×10^-7
脉冲数	128

	传统方法		分类别准确率/%	准确率/%	AANN		分类别准确率/%	准确率/%
	判定为目标	判定为杂波	分类别准确率/%	准确率/%	判定为目标	判定为杂波	分类别准确率/%	准确率/%
实际为目标	4 548	437	91.23	90.15	4 730	255	94.88	92.15
实际为杂波	548	4 467	89.07		475	4 540	89.71

	传统方法		分类别准确率/%	准确率/%	AANN		分类别准确率/%	准确率/%
	判定为目标	判定为杂波	分类别准确率/%	准确率/%	判定为目标	判定为杂波	分类别准确率/%	准确率/%
实际为目标	4 148	852	82.96	87.76	4 443	557	88.86	92.98
实际为杂波	372	4 628	92.56		145	4 855	97.10

	CPU	CPU+GPU
传统方法	1.917 77	1.898 992
AANN	0.316 548	0.011 944

Attention autocorrelation mechanism-based residual clutter suppression method

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