A Sparse Bayesian Learning-Based Approach With Indian Buffet Process Prior for Joint Wideband DOA and Frequency Band Estimation

Sparse Bayesian learning (SBL)-based methods for wideband direction of arrival (DOA) estimation have shown impressive performance in terms of high-resolution. It generally assumes that all signals share the same frequency band, resulting in performance degradation when signals occupy different frequency bands. To deal with this problem, we propose a wideband DOA estimation method utilizing the Indian Buffet Process (IBP) prior, namely IBP-SBL, to explore the frequency band correlation structure of target signals and hence their DOAs. Specifically, IBP-SBL regards the spatial spectrum as the combination of multiple latent features to be estimated, and infers the occupied frequency band of each signal by exploring the activation of the latent features at each frequency point. Subsequently, the DOA refinement procedure based on maximum likelihood (ML) is applied to reduce the quantization error caused by grid mismatch problem. Compared to the previous methods, the proposed algorithm can associate each sub-band with the relevant signals even in scenarios with severe overlap of signal frequency bands, thereby realizing more accurate DOA estimation by using the estimated frequency band related to each signal. Numerical simulation results of 500 Monte Carlo experiments show that our method achieves lower DOA estimation error and convergence time. In real-world data experiments, our method also exhibits superior DOA estimation accuracy and clearer target trajectory.