Robust direction-of-arrival estimation based on sparse asymptotic minimum variance

This study proposes a direction-of-arrival (DOA) estimation algorithm named robust sparse asymptotic minimum variance (RSAMV) to solve the current DOA algorithms' problems, such as the difficulty in weak target estimation, low resolution and the incapacity of separating coherent signal estimation. Through utilising a virtual weak target, the algorithm carries out dynamic diagonal loading to the sampling covariance matrix of SAMV in the iterative process, which effectively reduces weak target loss. Meanwhile, showing the feature of ultra-low side lobe and high sparseness, the spatial spectrum of RSAMV can easily achieve the high-resolution estimation of space target in the circumstances of coherent interference. Simulation results show that, compared with other algorithms, the RSAMV algorithm has higher spatial resolution ability and weak target detection ability. Its spatial spectrum has higher sparseness than other sparse algorithms and its performance is more robust than other SAMV algorithms. The Bering-Time Recording map processed by results that experiment on sea demonstrate the superiority of RSAMV algorithm.