【摘要】 认知雷达常用于完成探测、跟踪、成像及识别等多重任务,为提高其综合性能,需兼顾多方面因素研究其波形优化设计问题。基于此,提出一种基于压缩感知(CS)RIPless准则的步进频率信号认知雷达波形优化设计方法。首先,建立了目标回波信号稀疏模型,分析了其与发射信号模糊函数之间的关系。其次,根据模型中观测矩阵的构造,基于RIPless准则,将波形设计问题转化为概率分布的互相干参数及其协方差矩阵的条件数的优化问题,从而通过自适应寻优算法,获得优化的步进频率信号脉冲重复时间间隔序列和子脉冲频率序列。相较于传统方法,所提方法在信号发射与接收之间形成了信息实时反馈和信号优化重构的闭环,在高概率准确重构目标径向一维距离像的同时,也实现了发射信号模糊函数的优化。最后,仿真计算验证了所提方法的有效性。更多还原

【Abstract】 Cognitive radar is applied to diverse tasks,such as searching,tracking,imaging and recognition.To improve its comprehensive performance,various factors need to be considered to optimize the waveform.In this paper,a waveform optimization method for cognitive radar with stepped-frequency signal is proposed based on the RIPless compressed sensing(CS).Firstly,a sparse model of echo signal is established and the relationship between this model and the ambiguity function of transmitting signal is also analyzed.Secondly,on the basis of the RIPless compressed sensing,the waveform design translates to the optimization for the correlation coefficients and the covariance matrix condition numbers of a certain distribution.Then,the optimized pulse repetition intervals and carrier frequencies can be obtained through an adaptive optimal algorithm.Compared with the traditional method,a closed loop from information feedback to reconstruction between the signal transmitting and receiving is developed.Moreover,it can reconstruct the target one dimensional radial range profiles accurately with high probability and optimize the ambiguity function of transmitting signal simultaneously.Finally,the effectiveness of the proposed method is proved by simulations.更多还原