基于互协方差稀疏重构的MIMO雷达低仰角估计算法

针对低空目标仰角估计时, 多径信号间的混叠严重影响雷达的测角性能的问题, 基于压缩感知理论的波达方向(direction of arrival, DOA)估计算法与多输入多输出(multi-input and multi-output, MIMO)雷达体制结合起来共同进行低空目标DOA估计的研究, 提出了一种基于互协方差矩阵稀疏重构的MIMO雷达低空目标DOA估计算法。首先, 对MIMO雷达多径接收信号广义匹配滤波后的虚拟矩阵向量化处理, 并针对向量化后虚拟孔径扩展带来运算量大的缺点, 通过降维处理来减少运算量; 然后利用多快拍数互协方差矩阵中的噪声独立不相关的优点, 降低噪声影响, 提高算法估计性能; 最后转化为凸优化问题进行稀疏恢复。仿真结果表明算法在直达信号与多径反射信号相互削弱的情况下, 仍能有效估计低空目标的仰角, 较L1-SVD和L1-SRACV算法对低空目标具有更好的仰角估计性能。

Low elevation angle estimation algorithm for MIMO radar based on sparse reconstruction of cross-covariance

For the estimation of the elevation angle of low-altitude targets,the aliasing among multipath signals seriously affects the angle measurement performance of the radar.The direction of arrival(DOA)estimation algorithm based on compressed sensing theory and the multi-input and multi-output(MIMO)radar system are combined to apply to the DOA estimation of low-altitude targets,and a low-altitude target DOA estimation algorithm for MIMO radar based on sparse reconstruction of cross-covariance matrix is proposed.Firstly,the vectorization of the virtual matrix after the generalized matched filtering of the multipath received signal for the MIMO radar is processed.In view of the disadvantages of large computation caused by vectorization of virtual aperture expansion,the dimension reduction processing is carried out to reduce the computation.Then,the advantage of noise independence and uncorrelation in the cross-covariance matrix of multiple snapshots is utilized to reduce the impact of noise and improve estimation performance of the algorithm.Finally,the problem is transformed into convex optimization problem for sparse recovery.Simulation results show that this algorithm can still effectively estimate the elevation angle of low-altitude targets even when the direct signal and the multipath reflected signal are weakened each other,which has better performance in estimating the elevation angle of low-altitude targets Compared with L1-SVD and L1-SRACV algorithms.