摘要
由于有限的目标个数相对雷达的二维扫描空间高度稀疏,基于压缩感知理论,从稀疏信号重构角度对双基地MIMO雷达目标进行定位。首先,用收发阵列扫描的二维空间构建完备字典,多个目标在完备字典上的投影构成稀疏矩阵。其次,推导了双基地MIMO雷达的稀疏接收信号模型,并将接收信号模型矢量化。然后,建立加权l2范数最小化约束模型,并将约束模型转变为非约束模型,递归求解隐函数形式下双基地MIMO雷达目标参数的全局最优解。仿真结果表明,递归加权l2范数算法在90×90的密集网格和低信噪比环境下,可以实现双基地MIMO雷达多个稀疏目标的波达方向、波离方向和反射系数的联合估计,实现稀疏目标定位,而且目标各参数自动匹配。仿真结果验证了算法的有效性。
Limited number of radar targets in the two-dimensional scanning space is highly sparse.Based on the compressed sensing theory,targets of bistatic multiple-input and multiple-output(MIMO)radar are located from the perspective of sparse signal restoration in this paper.Firstly,an complete dictionary is constructed with two-dimensional space scanned by the transceiver arrays.The projections of multiple targets on the over-complete dictionary constitute a sparse matrix.Secondly,sparse signal model of bistatic MIMO radar is presented and is transformed into a vector.Then,minimization constraint model using weighted l2 norm is established and is transformed into a non-constrained model.The global optimal solution of targets parameters in implicit function is recursively solved.Simulation results show that even in the environment of 90×90 dense grids and low Signal Noise Ratio(SNR),three parameters including the direction of arrival(DOA),the direction of departure(DOD)and reflection coefficient of multiple sparse targets of bistatic MIMO radar can be accurately estimated.The three parameters are automatically paired and multiple sparse targets of bistatic MIMO radar are located.Simulation results verify the validity of the algorithm.
作者
赵霞
郭陈江
丁君
ZHAO Xia;GUO Chen-jiang;DING Jun(School of Electronics and Information,Northwestern Polytechnical University,Xi'an Shansi 710129,China;School of Electrical and Information Engineering,North Minzu University,Yinchuan Ningxia 750021,China)
出处
《计算机仿真》
北大核心
2020年第6期22-25,共4页
Computer Simulation
基金
总装预研重点基金(9140A01010412HK03004)。
