利用阵列协方差矩阵稀疏性的到达角估计方法

为了解决传统基于阵列协方差矩阵稀疏性到达角估计方法计算复杂度高的问题,提出基于直接二维稀疏重构思想的高效到达角估计方法。该方法利用阵列输出数据的协方差矩阵构造二维稀疏表示模型,对协方差矩阵进行特征值分解以实现噪声功率估计,从而降低噪声对到达角估计的影响。在求解稀疏表示模型时,直接对该二维稀疏重构问题进行求解,避免了矩阵矢量化操作。仿真实验结果表明,该方法运行效率大大提高,并且在低快拍数、低信噪比和稀疏阵元等条件下估计性能优于传统方法。

Directional-of-arrival estimation using the sparse representation of array covariance matrix

In order to improve the efficiency of conventional DOA (directional-of-arrival) estimation methods based on the sparse representation of array covariance, an efficient DOA estimation method relying on the direct 2D sparse reconstruction was proposed. The 2D sparse reconstruction model was constructed by using the array covariance matrix. The noise power can be estimated and the influence of noise on DOA estimation can thus be reduced by applying the eigenvalue decomposition. In solving the 2D sparse reconstruction problem, the 2D-SL0 (2D smoothed L0 norm) algorithm was used, which can deal with the 2D data directly, free of matrix vectorization operation. Simulation results show that the efficiency of the proposed method can be improved significantly, and the performance of the proposed method is better than traditional methods under the conditions of low snapshot, low SNR and sparse array sensors, etc.