基于压缩感知的多频率信号融合

传统多频带雷达信号融合是利用多个连续采样的子带信号来重构全频带信号,从而提高距离向分辨力,改善一维距离像质量。但是由压缩感知原理可知,采样矩阵与测量矩阵不相关性越大,全频带信号就能重构得越好,因此理论上基于随机采样的信号融合的性能要优于基于多个连续采样的信号融合。基于压缩感知原理将传统的多频带融合问题推广为任意随机采样的信号重构问题,利用基追踪方法来重构全频率信号,并给出了能够高概率成功重构的充分条件。通过实验也证明了这种随机采样融合的优越性。

Multi-frequency Fusion Based on Compressive Sensing

Traditional multi-frequency radar signal fusion can reconstruct full-band signal from some continuous sampled sub-band signals, and can improve range resolution and quality of range profile. But by the theory of Compressive Sensing, the more the incoherence between sampled matrix and measurement matrix is, the better the full-band signal can be reconstructed. Therefore, the performance of signal fusion based on random sampling is better than that of fusion based on continuous sampled theoretically. In the current study, traditional multi-band signal fusion was extended to random sampled signal reconstruction, and Basic Pursuit was used for reconstructing full-frequency signal. Then, the sufficient condition of successful reconstruction with high probability was presented. The experimental results prove the advantage of this fusion method.