随机混沌感知矩阵及其在成像雷达中的应用

Stochastic Chaotic Sensing Matrix and Its Application in Imaging Radar

根据压缩感知理论提出了一种适用于成像雷达的新算法,在成像目标分布满足稀疏性前提下,利用发射的随机混沌序列(SCS)形成卷积矩阵,然后通过随机行抽取构造随机感知矩阵(SCSM)。给出了完整的算法实现框架,从理论上证明了SCS的随机性和统计独立性以及SCSM的有限等距性(Restricted Isometry Property,RIP)。仿真结果验证了算法的有效性,同时分析了影响算法性能的主要因素。与匹配滤波法相比,所提算法重构误差小,输出旁瓣低。SCSM与其他随机矩阵具有相同的性能,然而,SCSM容易在硬件上实现,且更适用于要求保密性高和抗干扰能力强的场合。

A novel algorithm is proposed based on compressed sensing for imaging radar,in which,targetsin scene satisfy the requirement of sparsity peculiarity,and stochastic chaotic sensing matrix（ SCSM） isconstructed by selecting the rows of convolution matrix randomly,and columns of SCSM are stochastic chaotic sequences（ SCS）. The whole processing of this algorithm is presented. Moreover,it is theoreticallyproved that the SCS are random and statistically independent,and the SCSM satisfies the restricted isometryproperty（RIP）. Simulation results demonstrate the effectiveness of this algorithm,and factors highly influencing on the results are analyzed. In contrast to matched filter processing,the reconstruction error of theproposed algorithm is significantly reduced and sidelobes are faithfully suppressed. The SCSM possessesthe same performance as the other random matrices,however,it can be easily implemented in hardware andis more suitable for those occasions where security and strong anti-jamming ability is required.