Direction-of-arrival estimation of a modified sparse asymptotic minimum variance approach

A Direction-of-Arrival (DOA) estimation algorithm named the power factor variable Sparse Asymptotic Minimum Variance (SAMV-α) is proposed in this paper. The super-resolution DOA estimation, ultra low side lobe and coherent processing performance of SAMV-α algorithm are able to be obtained after altering the power factor of the algorithm in each iteration by means of a compromise parameter which is used to compromise the maximum likelihood estimation and the sparse performance of directional spectrum. Moreover, there are no DOA pre-estimates of the incident signals and the number of sources to be required in this algorithm. And the value of compromise parameter is limited between 0 and 1 which is more explicit than the regular parameter applied in sparse signal processing algorithms. Computer simulations indicate that DOA estimation performance of SAMV-α algorithm surpasses the kind of beam scanning algorithms and subspace algorithms. Comparing with the same type of sparse signal processing algorithms, SAMV-α algorithm also reaches a better bering estimation accuracy of incident signals. Meanwhile, the performance of SAMV-α algorithm is improved by about 3 dB compared with SAMV-1 algorithm in terms of the resolving power of adjacent sound sources. The performance of DOA estimation of SAMV-α algorithm is also verified by the sea experiment results, which can provide a clearer Bering-Time Recording map.