Off-grid DOA estimation using temporal block sparse Bayesian inference
Hongyu Cui, Huiping Duan, Hao Liu · 2016
By considering the off-grid distance in the sparse reconstruction model, off-grid direction-of-arrival (DOA) estimation can achieve better performance. Most existing off-grid algorithms consider that the snapshots of each source are independent with each other. This contradicts with the realworld scenario, where sources often have temporal structures. To address this issue, we present a new off-grid DOA estimation method, which brings the temporal structures into the off-grid model and a temporal block sparse Bayesian inference is derived. In comparison with the off-grid block sparse Bayesian inference method, the proposed approach achieves higher estimation accuracy for off-grid source directions in low SNR situations. Numerical simulations demonstrate the preferable performance of our method.