Simulation of Sparse Representation DOA Estimation Algorithm for Broadband Signal Covariance Matrix

In this article, we present a novel DOA (Direction of Arrival) estimation algorithm based on sparse representation of broadband signal covariance matrices. This algorithm leverages cutting-edge signal processing techniques and advanced matrix computations to achieve more accurate estimation of signal arrival directions. Specifically, the algorithm represents the signal covariance matrix as a sparse matrix and extracts DOA estimates along with signal strength information through matrix decomposition techniques. The implementation typically involves constructing a sparse representation model using l1-norm optimization techniques and solving it through algorithms like Orthogonal Matching Pursuit (OMP) or Basis Pursuit. Our simulation results demonstrate the algorithm's superior performance and feasibility across various scenarios, validated through MATLAB implementations featuring key functions such as sparse matrix construction, covariance matrix estimation, and decomposition algorithms. This research holds significant importance for the advancement of signal processing and communication technologies, particularly in array signal processing applications where computational efficiency and estimation accuracy are crucial.