摘要
针对双基地稀疏阵列MIMO雷达目标定位问题,该文提出一种基于投影处理与奇异值分解的多测量矢量欠定系统正则化聚焦求解(Projection-SVD-RMFOCUSS,PSVDRMF)算法。该算法首先估计接收角,接着依次将回波信号向目标存在的角度进行投影,最后将投影后的数据重排进行发射角估计,从而得到目标的准确位置。同时借助奇异值分解(SVD)进行信号降维与能量积累,进一步降低运算量,提高了传统压缩感知恢复算法在低信噪比下的估计性能。与现有稀疏重建算法相比,该算法减少了2维场景带来的庞大运算负担,且保持了良好的性能,可以稳健地对相干与非相干目标进行定位。
To solve the problem of target localization with sparse array in bistatic MIMO radar,a projection and Singular Value Decomposition（SVD） based Regularized Multi-vectors FOCal Undetermined System Solver（RMFOCUSS） algorithm is proposed.First the target angles with respect to receive array are estimated,and then the echoed signal is projected back to them.After an rearrangement of the projected signal,the target angles with respect to transmit array are estimated,so targets are located.SVD is utilized to reduce signal dimension and accumulate signal power,which makes traditional Compressive Sensing（CS） recovery algorithms perform better under low SNR,and computational complexity is reduced even more.Compared with existing sparse reconstruction approaches,the proposed method costs much less computation time in coping with large two dimensional scene and maintains a good performance whether the targets are relative or not.
出处
《电子与信息学报》
EI
CSCD
北大核心
2013年第5期1142-1148,共7页
Journal of Electronics & Information Technology
关键词
双基地MIMO雷达
稀疏阵列
压缩感知
多测量矢量欠定系统正则化聚焦
正交投影
Bistatic MIMO radar
Sparse array
Compressive Sensing（CS）
Regularized Multi-vectors FOCal Undetermined System Solver（RMFOCUSS）
Orthogonal projection
