High-resolution DOA estimation for closely spaced correlated signals using unitary sparse Bayesian learning

A novel method is proposed to effectively solve the challenging problem of direction-of-arrival (DOA) estimation for closely spaced correlated signals. A centro-Hermitian extended matrix is exploited to double the number of data samples, and then is transformed into a real-valued data matrix. An improved sparse Bayesian learning scheme is utilised to estimate DOAs by recovering the real-valued jointly row-sparse solution matrix with a reduced computational burden. The proposed method not only provides increased estimation accuracy but also has improved angular separation performance. Simulation results validate the effectiveness of the proposed method.