Extended-aperture DOA estimation based on Fourth-order cumulants using sparse Bayesian learning

Abstract

In this paper, we present a novel extended-aperture direction of arrival (DOA) estimation algorithm based on fourth-order cumulants (FOC) using sparse Bayesian learning. Firstly, A FOC sparse model which indicates signal directions is derived. Then the sparse Bayesian learning (SBL) technique is employed to estimate the DOAs. The reconstructed FOC vector extends the array aperture and improves the signal-to-noise ratio (SNR). So the proposed method can resolve more sources with lower computational complexity. To avoid the errors caused by mismatches between the true DOAs and the sampling grid, the deviations between the true DOAs and their nearest grid points are jointly estimated during the procedure. Simulation results demonstrate that the proposed algorithm shows superior performance in DOA estimation accuracy and computational efficiency.