基于加权l_1最小化的低复杂度波达方向估计算法

A low complexity algorithm based on the weighted l_1 inimization for DOA estimation

基于阵列协方差矩阵的稀疏表征和阵列响应矩阵的Khatri-Rao积,提出了一种低运算复杂度的波达方向估计算法.所提算法在减少未知数个数的同时,通过线性变换降低约束方程的维数,可有效减少优化问题的计算复杂度.为充分利用阵列协方差矩阵中蕴涵的信息,使用Capon谱的倒数作为权值构建出了加权l1最小化问题,这使得所提算法在降低运算量的同时能够获得较好的估计性能.仿真实验验证了所提算法的有效性.

Based on the sparse representation of the array covariance matrix and the Khatri-Rao product of the array response matrix,a low computational complexity sparse recovery method for direction-of-arrival(DOA)estimation is presented.The proposed algorithm not only lessens the number of unknown variable,but also can cut down the dimension of the constraints,which considerably reduce the computational complexity of the second order cone programming.Moreover,a weighted l1 minimization is designed by using the reciprocal of the Capon spectrum as a weighting vector.As a result,the proposed algorithm can achieve better performance while the computational complexity is reduced.Simulations demonstrate the performance of the proposed method.