Cognitive radar MIMO-STAP based on joint transmit andreceive weight optimization
基于联合收发权值优化的认知雷达MIMO-STAP

This paper studied the problem of joint transmit and receive weight optimization for cognitive radar MIMO-STAP. It proposed a method of joint space-time adaptive processingJSTAP both transmitter and receiver. It maximized the signal-to-interference-and-noise ratioSINR by optimizing the weights of transmitter and receiver jointly. It analyzed the structure of the clutter covariance affected by the weithts of transmitter, and built the iterative updating structure of weights of MIMO-STAP. The process was as follows:the first step was to find the weight of receiver by solving the optimizing model when the weight of transmitter was considered to be constant. The second step was to find the weight of transmitter based on the characteristics of the clutter covariance matrix when the weight of receiver was considered to be constant. Then the next step was to go back to the first step, and the cycle would not stop until the optimum weight was solved. The experiment results show that JSTAP technology improves the SINR performance contrasting with the conventional STAP technology when the velocity of target is slow.