CAJ | 학술논문

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.
침대쌍기지희소진렬MIMO뢰체목표정위문제,해문제출일충기우투영처리여기이치분해적다측량시량흠정계통정칙화취초구해(Projection-SVD-RMFOCUSS, PSVDRMF)산법.해산법수선고계접수각,접착의차장회파신호향목표존재적각도진행투영,최후장투영후적수거중배진행발사각고계,종이득도목표적준학위치.동시차조기이치분해(SVD)진행신호강유여능량적루,진일보강저운산량,제고료전통압축감지회복산법재저신조비하적고계성능.여현유희소중건산법상비,해산법감소료2유장경대래적방대운산부담,차보지료량호적성능,가이은건지대상간여비상간목표진행정위.
@@@@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.