Channel Doppler Information-based Sparse Representation Model and Target Detection Method in Passive Radar
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摘要: 近年来，基于时域或子载波域数据的稀疏表示理论为正交频分复用(OFDM)波形外源雷达目标探测提供了新的方法，可以提高目标参数的分辨率。然而该应用还面临着一些难题，一方面较高分辨率要求下构建稀疏字典时，不仅需具有较长相干积累时间的参考信号样本，稀疏字典的矩阵维度也随之变高进而导致稀疏重建的计算成本很高；另一方面现有的稀疏模型大都未考虑直达波或强多径等杂波对弱目标回波的掩盖问题，对于杂波中的较低信噪比目标重建结果不稳定。在此基础上，该文利用OFDM波形外源雷达的信道多普勒信息，提出了一种不仅字典矩阵具有较低稀疏字典维度、可离线生成，且可实现杂波抑制的稀疏表示模型，利用该模型不仅可一次稀疏优化求解生成距离多普勒图实现目标探测，还能降低稀疏重建的迭代次数要求。最后基于仿真和实测结果验证了本文所提方法相较于时域或有效子载波域数据稀疏模型的目标探测性能优势。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. -
表 2 OMP计算复杂度
方法 基于时域稀疏模型的探测方法 基于有效子载波稀疏模型的探测方法 基于所提稀疏模型的探测方法 计算复杂度 ${O}(4L{N_{\rm e}}MG)$ ${O}(4L{N_{\mathrm{v}}}MG)$ ${O}(8LR{f_{\max }}{N_{\rm u}}M{G_{\mathrm{c}}}/f)$ 
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