Channel Doppler Information-based Sparse Representation Model and Target Detection Method in Passive Radar

ZHAO Zhixin; LIN Yingyun; ZHENG Yiqun; ZHOU Huilin

doi:10.11999/JEIT250076

Volume 47 Issue 6

Jun. 2025

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Article Navigation > Journal of Electronics & Information Technology > 2025 > 47(6): 1816-1825

ZHAO Zhixin, LIN Yingyun, ZHENG Yiqun, ZHOU Huilin. Channel Doppler Information-based Sparse Representation Model and Target Detection Method in Passive Radar[J]. Journal of Electronics & Information Technology, 2025, 47(6): 1816-1825. doi: 10.11999/JEIT250076

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ZHAO Zhixin, LIN Yingyun, ZHENG Yiqun, ZHOU Huilin. Channel Doppler Information-based Sparse Representation Model and Target Detection Method in Passive Radar[J]. Journal of Electronics & Information Technology, 2025, 47(6): 1816-1825. doi: 10.11999/JEIT250076

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Channel Doppler Information-based Sparse Representation Model and Target Detection Method in Passive Radar

doi: 10.11999/JEIT250076 cstr: 32379.14.JEIT250076

School of Information Engineering, Nanchang University, Nanchang 330031, China

Funds: The National Natural Science Foundation of China(62261036), The Natural Science Foundation of Jiangxi Province (20224BAB202003, 20242BAB23010)

Received Date: 2025-02-12
Rev Recd Date: 2025-05-30

Available Online: 2025-06-11

Publish Date: 2025-06-30

Abstract

Abstract

Objective In passive radar systems based on Orthogonal Frequency Division Multiplexing (OFDM) waveforms, conventional target detection applies clutter suppression followed by parameter estimation using a Range-Doppler (RD) map derived from the mutual ambiguity function between surveillance and reference channel signals. However, this method yields low parameter resolution. Recent advances in sparse representation theory—applied to time-domain or subcarrier-domain data—have enabled higher-resolution target detection in OFDM-based passive radar. Despite this progress, several challenges remain. First, constructing a high-resolution sparse dictionary requires longer-coherence reference signal samples, which significantly increases dictionary dimensionality and computational cost in sparse reconstruction. Second, weak target echoes are often masked by clutter, such as direct-path signals and strong multipath components, which are typically not considered in current models. Therefore, reconstruction performance becomes unstable under low signal-to-noise Signal-to-Noiseratio (SNR) conditions. Methods This study proposes a novel sparse representation model for OFDM waveform passive radar that achieves clutter suppression and reduced dictionary dimensionality. The dictionary can be generated offline and facilitates target detection using channel Doppler information. Based on this model, Range-Doppler (RD) maps are constructed through a single sparse optimization process, reducing the number of iterations required for sparse reconstruction. The method first estimates the frequency-domain channel response of the detection scene by modeling the surveillance channel signal in both the time domain and the effective subcarrier domain. Given that direct-path and multipath clutter typically exhibit zero Doppler frequency shift—unlike target echoes—clutter suppression is achieved by subtracting the average channel response from the observed channel response. Channel Doppler analysis is then applied to obtain a sparse representation model based on the clutter-suppressed channel Doppler information. Finally, target detection is performed by introducing sparse constraints and executing sparse reconstruction. Results and Discussions Both simulation and experimental results are demonstrated to evaluate the target detection performance of the proposed method in comparison with time-domain and effective subcarrier-domain sparse models. Simulation results indicate that the proposed sparse model enables detection of targets at lower Signal-to-Noise Ratios (SNRs) than the other two models. Quantitative analysis shows that the Peak SideLobe Ratio (PSLR) and Integrated SideLobe Ratio (ISLR) achieved by the proposed method are approximately 1 dB and 1.5 dB lower, respectively, than those obtained using the time-domain and subcarrier-domain approaches. Furthermore, the computational complexity of the proposed method is significantly reduced—by 98.4% and 97.6% compared to the time-domain and subcarrier-domain models, respectively. This efficiency is attributed to the ability to generate the sparse dictionary matrix once offline, enhancing suitability for real-time applications. The experimental results further validate the superior target detection performance of the proposed method. Conclusions To address the challenges of high computational complexity in sparse reconstruction and the masking of weak targets by strong clutter, this study proposes a sparse representation model based on channel Doppler information, leveraging the signal characteristics of OFDM-based passive radar. Sparse constraints are incorporated into the model to enable effective target detection via sparse reconstruction. The dictionary matrix can be generated offline, which substantially reduces its dimensionality. This approach not only lowers the computational cost associated with high-resolution processing and extended integration times but also alleviates the masking effect of strong clutter on weak targets. Simulation results demonstrate that the proposed method achieves reliable detection of weak targets in multi-target scenarios while significantly reducing computational complexity. Performance is quantitatively evaluated using PSLR and ISLR, both of which are lower than those of existing time-domain and subcarrier-domain methods. In addition, experimental results using real data in complex clutter environments confirm the practical effectiveness of the proposed approach.
- Passive radar,
- Sparse representation model,
- Clutter suppression,
- OFDM waveform,
- Channel Doppler information

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References(20)

References

[1]	COLONE F, FILIPPINI F, and PASTINA D. Passive radar: Past, present, and future challenges[J]. IEEE Aerospace and Electronic Systems Magazine, 2023, 38(1): 54–69. doi: 10.1109/MAES.2022.3221685.
[2]	SUN Quande, SHAN Tao, FENG Yuan, et al. A frequency domain clutter suppression approach for passive radar[J]. IEEE Sensors Journal, 2024, 24(9): 14916–14929. doi: 10.1109/JSEN.2024.3382103.
[3]	万显荣, 易建新, 占伟杰, 等. 基于多照射源的被动雷达研究进展与发展趋势[J]. 雷达学报, 2020, 9(6): 939–958. doi: 10.12000/JR20143. WAN Xianrong, YI Jianxin, ZHAN Weijie, et al. Research progress and development trend of the multi-illuminator-based passive radar[J]. Journal of Radars, 2020, 9(6): 939–958. doi: 10.12000/JR20143.
[4]	WANG Xiaojiang and ZHANG Zhenkai. Joint range and velocity estimation for orthogonal frequency division multiplexing-based high-speed integrated radar and communications system[J]. IET Communications, 2022, 16(9): 1005–1019. doi: 10.1049/cmu2.12403.
[5]	HUANG Chuan, LI Zhongyu, AN Hongyang, et al. Maritime moving target detection using multiframe recursion association in GNSS-based passive radar[J]. IEEE Sensors Journal, 2024, 24(5): 6380–6391. doi: 10.1109/JSEN.2023.3343924.
[6]	SAMCZYŃSKI P, ABRATKIEWICZ K, PŁOTKA M, et al. 5G network-based passive radar[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60: 5108209. doi: 10.1109/TGRS.2021.3137904.
[7]	YI Jianxin, WAN Xianrong, and LEUNG H. ℓ₀-regularized least squares versus matched filtering[J]. Signal Processing, 2022, 192: 108398. doi: 10.1016/j.sigpro.2021.108398.
[8]	全英汇, 吴耀君, 段丽宁, 等. 基于稀疏恢复的雷达信号处理研究综述[J]. 雷达学报, 2024, 13(1): 46–67. doi: 10.12000/JR23211. QUAN Yinghui, WU Yaojun, DUAN Lining, et al. A review of radar signal processing based on sparse recovery[J]. Journal of Radars, 2024, 13(1): 46–67. doi: 10.12000/JR23211.
[9]	LAI Hongjun, YE Kun, SUN Haixin, et al. Atomic norm-based joint delay-doppler shift estimation for OFDM passive radar[J]. IEEE Signal Processing Letters, 2024, 31: 36–40. doi: 10.1109/LSP.2023.3341391.
[10]	史海旭, 徐仲秋, 李光祚, 等. 利用稀疏CP-OFDM的SAR抗干扰成像方法研究[J]. 电子与信息学报, 2024, 46(12): 4441–4450. doi: 10.11999/JEIT240092. SHI Haixu, XU Zhongqiu, LI Guangzuo, et al. Research on SAR anti-jamming imaging method with sparse CP-OFDM[J]. Journal of Electronics & Information Technology, 2024, 46(12): 4441–4450. doi: 10.11999/JEIT240092.
[11]	WU Min and HAO Chengpeng. Super-resolution TOA and AOA estimation for OFDM radar systems based on compressed sensing[J]. IEEE Transactions on Aerospace and Electronic Systems, 2022, 58(6): 5730–5740. doi: 10.1109/TAES.2022.3178393.
[12]	NIKAEIN H, ZEFREH R G, and GAZOR S. Target detection and interference cancellation in passive radar sensors via group-sparse regression[J]. IEEE Sensors Journal, 2023, 23(18): 21484–21492. doi: 10.1109/JSEN.2023.3300887.
[13]	NIKAEIN H, SHEIKHI A, and GAZOR S. Target detection in passive radar sensors using least angle regression[J]. IEEE Sensors Journal, 2021, 21(4): 4533–4542. doi: 10.1109/JSEN.2020.3035630.
[14]	WEN Jinfang, YI Jianxin, and WAN Xianrong. Sparse representation for target parameter estimation in CDR-based passive radar[J]. IEEE Geoscience and Remote Sensing Letters, 2021, 18(6): 1024–1028. doi: 10.1109/LGRS.2020.2991743.
[15]	赵志欣, 朱斯航, 洪升, 等. 基于数字调幅广播外辐射源雷达的弱目标检测[J]. 兵工学报, 2017, 38(11): 2159–2165. doi: 10.3969/j.issn.1000-1093.2017.11.011. ZHAO Zhixin, ZHU Sihang, HONG Sheng, et al. Weak target detection of DRM-based passive bistatic radar[J]. Acta Armamentarii, 2017, 38(11): 2159–2165. doi: 10.3969/j.issn.1000-1093.2017.11.011.
[16]	李文静, 李卓林, 袁振涛. 基于稀疏重构的海杂波抑制和目标提取算法[J]. 系统工程与电子技术, 2022, 44(3): 777–785. doi: 10.12305/j.issn.1001-506X.2022.03.09. LI Wenjing, LI Zhuolin, and YUAN Zhentao. Sea clutter suppression and target extraction algorithm based on sparse reconstruction[J]. Systems Engineering and Electronics, 2022, 44(3): 777–785. doi: 10.12305/j.issn.1001-506X.2022.03.09.
[17]	白宗龙, 师黎明, 孙金玮. 基于自适应LASSO先验的稀疏贝叶斯学习算法[J]. 自动化学报, 2022, 48(5): 1193–1208. doi: 10.16383/j.aas.c210022. BAI Zonglong, SHI Liming, and SUN Jinwei. Sparse Bayesian learning using adaptive LASSO priors[J]. Acta Automatica Sinica, 2022, 48(5): 1193–1208. doi: 10.16383/j.aas.c210022.
[18]	SAHOO S K and MAKUR A. Signal recovery from random measurements via extended orthogonal matching pursuit[J]. IEEE Transactions on Signal Processing, 2015, 63(10): 2572–2581. doi: 10.1109/TSP.2015.2413384.
[19]	LELLOUCH G, MISHRA A K, and INGGS M. Design of OFDM radar pulses using genetic algorithm based techniques[J]. IEEE Transactions on Aerospace and Electronic Systems, 2016, 52(4): 1953–1966. doi: 10.1109/TAES.2016.140671.
[20]	COLONE F, O'HAGAN D W, LOMBARDO P, et al. A multistage processing algorithm for disturbance removal and target detection in passive bistatic radar[J]. IEEE Transactions on Aerospace and Electronic Systems, 2009, 45(2): 698–722. doi: 10.1109/TAES.2009.5089551.