针对L1-SRACV算法在低快拍时波达方向估计性能严重下降的问题,分析其原因并提出一种基于快速极大似然算法的波达方向估计新方法.首先利用快速极大似然算法估计协方差矩阵,以解决由于快拍数较低引起协方差矩阵小特征值不稳定的问题.然后建立了基于快速极大似然算法的稀疏模型进行波达方向估计.最后,为了进一步提高算法在快拍数较小时的性能,选择剔除协方差矩阵的对角元素,并对建立的波达方向估计模型进行了修改.仿真结果表明,所提算法相对于L1-SRACV算法具有高的估计精度和检测概率,尤其是在快拍数较小时仍能获得高的估计精度.%The performance of the L1-norm-based sparse representation of array covariance vectors(L1-SRACV) algorithm significantly degrades with the number of samples decreasing. This paper analyzes the essential cause of this performance degradation and proposes a new direction of arrival(DOA) estimation method based on the fast maximum likelihood(FML) algorithm. Firstly, the FML algorithm is employed to estimate the covariance matrix, which attenuates the instability of the small eigenvalues of the covariance matrix. Then the sparse representation model based on the FML is formulated for DOA estimation and finally, optimized by removing the diagonal elements of the covariance matrix to obtain better performance. Simulation results indicate that our method outperforms the L1-SRACV with a higher accuracy and detection possibility, particularly under small samples support.
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