The Real Valued Sparse Direction of Arrival (DOA) Estimation Based on the Khatri Rao Product
There is a problem that complex operation which leads to a heavy calculation burden is required when the direction of arrival (DOA) of a sparse signal is estimated by using the array covariance matrix. The solution of the multiple measurement vectors (MMV) model is difficult. In this paper, a real valued sparse DOA estimation algorithm based on the Khatri Rao (KR) product called the L1 RVSKR is proposed. The proposed algorithm is based on the sparse representation of the array covariance matrix. The array covariance matrix is transformed to a real valued matrix via a unitary transformation so that a real valued sparse model is achieved. The real valued sparse model is vectorized for transforming to a single measurement vector (SMV) model, and a new virtual overcomplete dictionary is constructed according to the KR product's property. Finally, the sparse DOA estimation is solved by utilizing the idea of a sparse representation of array covariance vectors (SRACV) The simulation results demonstrate the superior performance and the low computational complexity of the proposed algorithm.
Sensors