基于图特征学习的海杂波抑制算法

doi:10.12382/bgxb.2021.0662

摘要/Abstract

摘要：

为有效降低海杂波对海洋雷达的工作影响,研究海杂波的抑制问题,提出一种基于图特征学习的海杂波抑制算法。使用时频变换对雷达回波信号进行维度扩增,基于图嵌入处理深度挖掘图结构特征的思想,并依据海杂波和目标回波信号在时频谱中的不同结构特性,给出一种通过图嵌入进行信号节点特征向量构造的方法。区别于传统时域对消和子空间分解等方法,该方法可以通过时频谱中不同信号的节点分类实现海杂波的抑制。仿真与实测结果表明,该算法可以有效抑制雷达回波信号中的海杂波分量,提升雷达回波信号的信杂比,为海洋雷达进行海杂波的抑制提供了新的思路和途径。

关键词: 海杂波抑制, 时频变换, 图嵌入, 图结构, 节点分类

Abstract:

In order to reduce the effect of sea clutter on Marine radar,a sea clutter suppression algorithm based on graph feature learning is proposed. The time-frequency transform is used to amplify the dimension of radar echo signal,and based on the idea that the graph structure features can be deeply mined by graph embedding processing, a method of constructing signal node feature vector by graph embedding is presented according to the different structural characteristics of sea clutter and target echo signal in time spectrum. Different from traditional methods such as time domain cancellation and subspace decomposition,this method can be used to implement sea clutter suppression through node classification of different signals in the time spectrum. Simulation and experimental results show that the algorithm can effectively suppress the sea clutter component of radar echo signal,improve the signal to clutter ratio of radar echo signal,and provide a new idea and way for ocean radar to suppress sea clutter.

Key words: sea clutter suppression, time-frequency transformation, graph embedding, graph structure, node classification

中图分类号:

TN957

陈鹏, 许震, 曹振新, 王宗新. 基于图特征学习的海杂波抑制算法[J]. 兵工学报, 2023, 44(2): 534-544.

CHEN Peng, XU Zhen, CAO Zhenxin, WANG Zongxin. Graph Feature Learning-Based Sea Clutter Suppression Method[J]. Acta Armamentarii, 2023, 44(2): 534-544.

图/表 16

图1 海洋雷达探测示意图

Fig.1 Schematic diagram of ocean radar detection

图2 时频谱的图构造流程

Fig.2 Graph construction flow of time spectrum

图3 遍历矩阵S的范围和所对应的邻接矩阵Ar

Fig.3 The range of the traversal matrix S and the corresponding adjacency matrix Ar

图4 邻接矩阵Ar对应的图Gr

Fig.4 Graph Gr of adjacency matrix Ar

图5 针对节点n2的两种搜索策略(步长k=3)

Fig.5 Two search strategies for nodes n2(step k=3)

图6 随机游走的搜索策略

Fig.6 Random walk search strategy

图7 海杂波抑制算法

Fig.7 Sea clutter suppression algorithm

表1 仿真参数设置

Table 1 Simulation parameter setting

参数	取值
目标回波信号中的目标个数K	{1,2,3}
目标回波信号的多普勒频率分量f_d/Hz	{50,75,100}
形状参数$\hat{a}$	0.7435
尺度参数$\hat{v}$	2.3281
超参数p和q	p∈{1,2,4},q∈{0.25,0.50}
节点特征向量的维度d	128
随机游走的次数r	10
分类参数n	{5,6,7}

表2 海杂波数据来源于仿真数据

Table 2 Sea clutter data comes from simulation data

相似度		多普勒频率/Hz
		50		75		100
		u₁	u₂	u₁	u₂	u₁	u₂
	{1,0.25}	0.62	0.29	0.63	0.21	0.94	0.07
	{2,0.25}	0.75	0.14	0.74	0.12	0.90	0.17
{p,q}	{4,0.25}	0.77	0.13	0.83	0.11	0.96	0.05
	{1,0.50}	0.65	0.22	0.69	0.20	0.73	0.18
	{2,0.50}	0.69	0.21	0.72	0.18	0.83	0.15
	{4,0.50}	0.75	0.25	0.80	0.17	0.87	0.14

表3 海杂波数据来源于实测数据

Table 3 Sea clutter data comes from measured data

相似度		多普勒频率/Hz
		50		75		100
		u₁	u₂	u₁	u₂	u₁	u₂
	{1,0.25}	0.60	0.30	0.62	0.23	0.91	0.08
	{2,0.25}	0.69	0.26	0.72	0.15	0.87	0.10
{p,q}	{4,0.25}	0.75	0.20	0.79	0.13	0.94	0.06
	{1,0.50}	0.64	0.25	0.62	0.29	0.72	0.20
	{2,0.50}	0.67	0.23	0.70	0.20	0.82	0.16
	{4,0.50}	0.71	0.22	0.79	0.19	0.75	0.21

图8 Macro-F1 score

Fig.8 Macro-F1 score

图9 Micro-F1 score

Fig.9 Micro-F1 score

图10 ISCR

Fig.10 ISCR

图11 单目标环境下使用海杂波抑制算法前后的时频谱对比

Fig.11 Time spectrum comparison before and after sea clutter suppression algorithm in single target environment

图12 4种算法进行海杂波抑制后的ISCR对比

Fig.12 Four algorithms are used to compare ISCR after sea clutter suppression

图13 多目标环境下使用海杂波抑制算法前后的时频谱对比

Fig.13 Time spectrum comparison before and after sea clutter suppression algorithm in multiple target environment

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