面向检测的认知雷达低峰均比稳健波形设计

Robust Detection Waveform Design for Cognitive Radar Under Low PAR Constraints by Convex Optimization

现有认知雷达的发射波形和接收机滤波器的设计方法中大都假定目标和杂波先验信息已知,且并未考虑实际发射机的非线性特性。针对该问题,在随机目标冲激响应和信号相关杂波先验信息均不准确的背景下,提出一种稳健的认知雷达低峰均比发射波形和接收机滤波器联合设计算法。该方法根据极小极大法构造信号模型,给出最差SINR下目标和杂波协方差矩阵不确定度的取值;在此基础上,采用半正定松弛(Semi-definite Relaxation,SDR)方法将非凸问题转化为一个凸问题进行求解,并通过循环迭代得到最优的发射波形和接收机滤波器。仿真结果验证所提方法的有效性。

The existing joint design methods of transmitting waveform and receiving filter for Cognitive Radar(CR)radar are all based on the precise previous information about the target and interference,the efficiency of transmitter is not taken into account in practical applications.For these problems,a low Peak-to-Average power Ratio(PAR)robust waveform and receiving filter design algorithm is proposed under the assumption of the random Target Impulse Response(TIR)and signal-dependent interference.So a signal model is constructed according to the Max-Min method and the value of the uncertainty region about the covariance matrix of TIR and the Clutter Impulse Response(CIR)under the worst-case Signal-to-Interference-And-Noise rate(SINR)is given.Then the Semi-Definite Relaxation(SDR)method is used to transform the nonconvex problem into a convex problem to maximize the worst-case SINR.On this basis,the optimal transmit waveform and receiver filter are obtained by using the cyclic iteration idea.The simulation results show the effectiveness of the proposed the method.