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.

Fast Sparse Bayesian Learning Based on Beamformer Power Outputs to Solve Wideband DOA Estimation in Underwater Strong Interference Environment

“…l updating is O(LK active K B ) if a basis is added to the active basis set in an iteration, while the computational workload becomes O LK 2 active if a basis is updated or deleted, where K active represents the number of elements in active basis set. It is obvious that the workload of the proposed method is smaller than that of the SBL-BPO since K active , K B < K G .…”

“…Direction-of-arrival (DOA) estimation is a major task in underwater signal processing [1], through which the sonar system can obtain the target position. In recent years, sparsity-based DOA estimation [1][2][3][4][5] has attracted much attention since it can be applied under a low signal-to-noise ratio (SNR) condition in comparison with traditional DOA estimation methods [1]. This characteristic helps the system work well even in a noisy environment [6].…”

Direction-of-Arrival Estimation via Sparse Bayesian Learning Exploiting Hierarchical Priors with Low Complexity

“…For the latter, three categories can be summarized out of massive documents. (i) The first is based on norm optimization [20][21][22][23][24][25][26][27][28], (ii) the second is exploiting basis pursuit [29,30], and (iii), and the third is utilizing sparse Bayesian learning (SBL) [31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47]. In particular, SBL-based methods are popular and highly favored owing to incomparable comprehensive performance beyond both norm optimization and basis pursuit [48].…”