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High-resolution Sparse Representation and Its Applications in Radar Moving Target Detection
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摘要: 复杂背景下稳健高效的低可观测动目标检测始终是雷达信号处理领域的研究热点和难点，一方面，强杂波背景和目标复杂运动使得信号微弱，时频域难以区分；另一方面，相参积累算法复杂，长时间积累运算量较大，如何利用有限的雷达资源提高雷达探测性能成为亟需解决的问题。高分辨稀疏表示技术从信号稀疏性角度出发区分杂波和动目标，是传统变换域动目标检测技术的拓展，具有高时频分辨率、对噪声不敏感、稳健性高以及适于多分量信号分析的优势，有广阔应用前景。该文重点从应用角度进行归纳总结，系统回顾了雷达动目标检测的常规方法，然后对稀疏表示在雷达杂波特性分析、抑制、动目标检测、特征提取、时频分析等方面的应用进行了初步总结和归纳，对研究方向进行展望，最后结合实测数据和已有成果给出了部分处理结果。Abstract: To address difficulties in radar signal processing, the effective and efficient detection of low-observable moving targets in complex environments is an ongoing research hotspot. On the one hand, a signal may be extremely weak due to strong clutter and the complex motion of a target, making it hard to separate them in the time and frequency domains. On the other hand, complex coherent integration methods and the heavy computational burden of long-time integration represent challenges for improving radar detection performance with limited resources. High-resolution sparse representation can separate clutter from a moving target with respect to signal sparsity, and can be regarded as an extension of traditional transform-based moving target detection methods. This method has promising application prospects due to the advantages of its high time-frequency resolution, anti-noise property, robustness, and suitability for the analysis of multi-signals. In this paper, we systematically review conventional radar moving target detection methods. Then, we summarize their applications, including sparse representation in clutter property analysis, suppression, moving target detection, signature extraction, and time-frequency analysis. Next, we consider future developments. Finally, we provide some results based on real datasets and existing research.
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图 3 海上机动目标的距离和多普勒徙动(X波段CSIR雷达数据)
Figure 3. Range and Doppler migrations of marine maneuvering target (X-band CISR data)
图 4 基于长时间相参积累的高速高机动目标检测方法
Figure 4. High-speed and maneuvering target detection based on long-time coherent integration
图 8 FFT与SFT的运算量对比分析(美国MIT实验室)
Figure 8. Comparison of computation cost between FFT and SFT (MIT Laboratory)
图 9 传统时频分析和稀疏时频分析技术的人体运动目标回波分析结果对比
Figure 9. Comparison of human movement analysis between traditional TFD and STFD
图 10 基于STFD的海上动目标检测结果对比(X波段CSIR雷达数据TFC17_006，切面图，Pfa=10–4)
Figure 10. Marine moving target detection comparisons via different STFDs (X-band CSIR datasets TFC17_006, slice plot, Pfa=10–4)
表 1 检测性能和计算时间对比(仿真机动目标+TFC17_006海杂波，采样点1024, Pfa=10–4)
Table 1. Comparison of detection performance and computational burden (Simulated moving target+TFC17_006 sea clutter, sampling number 1024, Pfa=10–4)
检测方法 MTD SFT FRFT SFRFT FRAF SFRAF 稀疏信号分量 – 13 – 10 – 2 Pd (SCR= –5 dB) 62.47% 68.35% 68.74% 70.21% 85.69% 89.35% 计算时间* (ms) 4.69 5.73 12.54 8.92 14.61 10.52 “*”：计算机配置：Intel Core i7-4790 3.6 GHz CPU; 16 G RAM; Matlab R2014a，计算时间为算法1次运算时间 
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