面向外辐射源雷达目标探测的非时变稀疏模型和深度展开网络实现方法

doi:10.12382/bgxb.2023.0579

摘要/Abstract

摘要：

近年来,基于稀疏特征提取的目标探测方法成为了雷达领域的研究热点。基于正交频分复用调制的外辐射源雷达(简称“外源雷达”)由于发射波形不受控,一方面构建的稀疏模型会随未知发射波形时变,导致相应的目标探测方法计算量大;另一方面目标回波常常因被直达波等强杂波掩盖而面临探测困难。在此背景下,利用外源雷达的正交频分复用波形特点,使用导频位置处频域信道响应提出一种非时变稀疏模型。通过将稀疏模型求解的每一次迭代过程替代为一层神经网络,首次研究了基于深度展开网络的智能化外源雷达目标探测实现方法。仿真和实测数据结果表明:所提方法与传统杂波抑制方法在目标探测上性能相近,但有着更低的计算复杂度,且无需人工设计稀疏矩阵等稀疏模型求解参数。

关键词: 外辐射源雷达, 正交频分复用波形, 目标探测, 稀疏模型, 深度学习

Abstract:

In recent years, the target detection method based on sparse feature extraction has become a research hotspot in radar field. However, due to the uncontrolled transmitted waveform of orthogonal frequency division multiplexing(OFDM)-based passive radar, the sparse model will change with the unknown transmitted waveform, resulting in a large amount of calculation and more manual intervention for the corresponding target detection method. On the other hand, it is difficult to detect target echo because it is often covered by strong clutter such as direct-path signal. In this context, a time-invariant sparse model is proposed by using the waveform characteristic of the OFDM-based passive radar and the channel frequency response at the pilot position. Then, a realization method of intelligent passive radar target detection based on the deep unrolling network is firstly studied by replacing each iteration process of the sparse model solution with a layer of neural network. Simulated and measured results show that the proposed method has similar performance to the traditional clutter suppression method in target detection, but it has lower computational complexity, and does not need to manually design the solving parameters such as sparse matrix of sparse model.

Key words: passive radar, orthogonal frequency division multiplexing waveform, target detection, sparse model, deep learning

中图分类号:

TN957.51

赵志欣, 曹玉龙, 陈远帅, 周辉林, 王玉皞. 面向外辐射源雷达目标探测的非时变稀疏模型和深度展开网络实现方法[J]. 兵工学报, 2024, 45(8): 2806-2816.

ZHAO Zhixin, CAO Yulong, CHEN Yuanshuai, ZHOU Huilin, WANG Yuhao. A Time-invariant Sparse Model and a Deep Unrolling Network for Target Detection of Passive Radar[J]. Acta Armamentarii, 2024, 45(8): 2806-2816.

图/表 11

图1 LISTA-CP网络的第k层带有可学习的参数θ(k)和W(k)

Fig.1 The kth layer of LISTA network with learnable parameters θ(k) and W(k)

图2 基于LISTA-CP网络的目标探测框图

Fig.2 The pipeline of LISTA-CP framework for target detection

图3 短波段的数字广播信号增益导频格式

Fig.3 Short-wave digital broadcast signal gain pilot pattern

表1 信道仿真参数

Table 1 Channel simulation parameters

信道参数	数值
一个符号子载波数	207
OFDM符号数	60
含有导频子载波数	2080
导频插入方式	梅花状导频
调制方式	64 QAM
采样频率/kHz	12
直达波信噪比/dB	40
多径条数	10~12
多径离散时延	0~39
多径信噪比/dB	0~30

表2 第1组实验中杂波仿真参数与LISTA-CP网络结果

Table 2 Clutter simulation parameters and LISTA-CP network results in two groups of Experiment 1

距离元	多普勒频移/Hz	杂噪比/dB
距离元	多普勒频移/Hz	预设值	网络估计值
0		40	40.006
1		29	28.980
4		26	25.950
5		25	24.970
6		22	22.020
10	0	21	20.930
11		20	19.900
14		14	14.010
18		13	12.430
22		10	9.260
30		9	7.670
37		7	5.160

表3 第2组实验中杂波仿真参数与LISTA-CP网络结果

Table 3 Clutter simulation parameters and LISTA-CP network results in two groups of Experiment 2

距离元	多普勒频移/Hz	杂噪比/dB
距离元	多普勒频移/Hz	预设值	网络估计值
0		40	40.005
1		29	29.005
5		27	26.830
8		26	26.230
11		20	20.320
12	0	17	17.270
13		16	16.160
15		13	12.940
18		11	10.830
24		9	7.780
37		5	4.009

图4 在目标信噪比为-15dB的仿真数据下不同方法获得的RD图

Fig.4 RD maps obtained by different methods for simulation data with target SNR of -15dB

图5 在目标信噪比为-20dB的仿真数据下不同方法获得的RD图

Fig.5 RD maps obtained by different methods for simulation data with target SNR of -20dB

图6 不同目标信噪比下的PSBR值

Fig.6 PSBR value versus target SNR

图7 在第1组实测数据下不同方法获得的RD图

Fig.7 RD maps obtained by different methods for the experimental data of Experiment 1

图8 在第2组实测数据下不同方法获得的RD图

Fig.8 RD maps obtained by different methods for the experimental data of Experiment 2

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