Wideband Direction-of-Arrival Estimation Based on Hierarchical Sparse Bayesian Learning for Signals with the Same or Different Frequency Bands

Abstract

Wideband sparse Bayesian learning (WSBL) based on joint sparsity achieves high direction-of-arrival (DOA) estimation precision when the signals share the same frequency band. However, when the signal frequency bands are non-overlapped or partially overlapped, i.e., the frequency bands are different, the performance of the method degrades due to the improper prior on signal. This paper aims at extending the WSBL to a more general version, which is also suitable for the cases where the signal frequency bands are non-overlapped or partially overlapped. Given that the signals are sparsely distributed in the space, the signal matrix whose column is composed of the signal in each frequency bin is row-sparse. Moreover, the signal vectors in some frequency bins have different sparse supports when the signals occupy the different frequency bands. Therefore, a hierarchical sparse prior is assigned to the signal matrix, where a set of hyperparameters are used to ensure the row-sparsity and the other set are used to adjust the signal sparsity in each frequency bin. The DOAs are finally estimated in the Bayesian framework. The simulation results verify that the proposed method achieves good performance on estimation precision in both the same and different frequency band scenarios.