针对非格点参数的双基地MIMO雷达联合稀疏恢复

将稀疏恢复应用于雷达参数估计中,在保证估计精度同时,可大幅减少空时采样个数;但当目标参数不在格点时,估计误差会明显恶化。针对双基地MIMO雷达中不在格点的参数估计问题,引入联合稀疏优化方法,构建结构化误差矩阵和块稀疏向量。推导了进行参数估计的修正表达式,然后利用块稀疏算法求解,可有效提高稀疏恢复的精度。为解决计算复杂性,引入快速KCS稀疏恢复算法。仿真表明,在典型恢复场景下,可明显提高参数估计精度;同时计算时间节省1个数量级以上。

The union sparse recovery for off-grid parameter estimation of bistatic MIMO radar

It can greatly reduce the sampling numbers in both time and frequency domains to apply the sparse recovery technique into the parameter estimation of radars. However, the results will distinctly deteriorate when the values of parameter to estimate are not exactly on the grid. To solve this problem in bistatic MIMO radar, we bring in the way of union sparse recovery, by building a constructive matrix and union sparse vector, then deriving the modified method, finally making use of the algorithm of block sparse recovery to get a better accuracy result. Considering the complexity of the proposed algorithm, the fast KCS algorithm is implanted. The simulations show that under a common scene the recovery accuracy can be improved greatly while the consuming time reduced by one order of magnitude..