Direction-of-Arrival Estimation Based on Enhanced Sparse Representation
Qiuxiang Shen, Bin Yih Liao, Huiping Huang · 2018
In this paper, we proposed an algorithm based on an enhanced sparse representation for direction-of-arrival (DOA) estimation. Different from existing approaches, in this method a weight vector is designed and applied to enforce the sparsity at the true source locations. More precisely, we first design a weight vector by making use of the spatial spectrum. Then, this weight vector is incorporated into the sparse representation framework for DOA estimation. Owing to the usage of the weight vector, the sparsity can be enhanced comparing to the existing methods. Hence, a better performance of DOA estimation can be achieved. Moreover, the uncertainty of the sample covariance matrix is taken into account to ensure the robustness. Numerical examples are conducted to validate the effectiveness and superiority of the proposed method.