A Time-invariant Sparse Model and a Deep Unrolling Network for Target Detection of Passive Radar

doi:10.12382/bgxb.2023.0579

Abstract

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

CLC Number:

TN957.51

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.

Figures/Tables 11

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

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

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

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

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

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

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

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

Fig.6 PSBR value versus target SNR

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

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

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