基于联合收发权值优化的认知雷达MIMO-STAP

研究了认知雷达中多收多发空时自适应处理(MIMO-STAP)联合收发权值优化问题。提出了一种在收发两端联合空时自适应处理(JSTAP)的方法,该方法通过对收发权值联合寻优以获得最优的信干噪比。分析了受发射权值影响的杂波协方差矩阵结构,并基于此建立了MIMO-STAP的权值迭代更新结构。其权值迭代更新步骤为:固定发射权值,求解优化模型得到接收权值;然后固定接收权值,根据杂波协方差矩阵与发射权值的关系,得到发射权值;返回接收权值优化步骤,循环迭代以获得最优收发权值。仿真实验结果表明在慢速目标环境中,联合空时处理与常规空时处理相比,有效提高了接收端的信干噪比。

Cognitive radar MIMO-STAP based on joint transmit andreceive weight optimization

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.