认知MIMO雷达发射波形与接收滤波器联合优化设计方法

为了突破传统雷达的性能瓶颈，具有发射端自适应机制的认知雷达成为了研究热点，其中发射波形优化设计是认知雷达的核心组成。为了提升机载平台下对运动目标的检测能力，考虑了多输入多输出雷达中发射波形与接收滤波器的联合设计问题，并使用交替优化方法进行求解。在求解最优接收滤波器时，优化问题被建模为经典的广义瑞利熵问题，对此使用最小方差无失真响应方法进行求解以避免特征值分解运算。在设计最优发射波形时，在最大化信杂噪比的准则下建立目标函数，并引入工程常用的恒模约束条件，最终将波形设计问题建模成一个非凸的优化问题。对此，经典的求解方法是先使用近似手段松弛恒模约束，然后使用Charnes-Cooper变换进行求解。区别于经典方法，提出一种基于信杂噪比近似的优化算法以减少计算复杂度。实验结果表明，所提方法不仅具有更快的收敛速度，还可以实现更优的信杂噪比增益性能。

Joint optimization design method for cognitive MIMO radar transmit waveform and receive filter

In order to break through the performance bottleneck of traditional radars, cognitive radar with self-adaptive mechanisms have become a research hotspot. Among them, transmit waveform design is the core component. In order to improve the detection ability of moving targets under the airborne platform, the joint design problem of transmit waveform and receive filter in the multiple input multiple output radar is considered, and an alternate optimization method is used to solve it. When calculating the optimal receiving filter, the optimization problem is modeled as a classic generalized Rayleigh entropy problem, for which the minimum variance distortionless response (MVDR) method is used to solve it. When designing the transmit waveform, the objective function is established under the criterion of maximizing the signal to clutter plus noise ratio (SCNR), and the constant modulus constraints are introduced, and finally the problem is modeled as a non-convex one. In this regard, the classic solution method uses approximate means to relax the constraint, and then uses the Charnes-Cooper transform to solve it. Different from this, an optimization algorithm based on SCNR approximation is proposed to reduce computational complexity. Experimental results show that the method proposed not only has a faster convergence rate, but also can achieve better SCNR gain performance.