Multi-frequency sequential sparse Bayesian learning for DOA estimation of the moving wideband sound source

Abstract

To obtain the Direction of arrival (DOA) of the moving sound source from the sequential measurements collected by the microphone array is the main task in acoustic tracking and detection. Thanks to the development of compressive sensing (CS) and sparse Bayesian learning (SBL), treating time-varying DOA estimation as time-varying sparse signal recovery is considered to be a promising idea. However, most methods have assumed that the source is narrowband and the DOA is on the predefined sparse grid at each estimation step. In fact, most sound sources in the air are wideband and the DOA variescontinuously. Therefore, the multi-frequency sequential sparse Bayesian learning (SBL) is proposed for the DOA estimation of the moving wideband sound source in this paper. In this method, gamma hyperprior is used as sparsity-promoting prior for multi-frequency bins so that the multi-frequency measurements can be utilized simultaneously, and with an inexact dynamic model, the sparsity-dependent information from the multi-frequency sequential measurements can be propagated to the next estimation step to improve the performance. Besides, the off-grid refinement is incorporated into the framework to adapt to the continuously varying DOA. Simulation results demonstrate that the proposed method has better performances under low signal-to-noise conditions with higher estimation accuracy and less computation time compared to other state-of-the-art methods. The field experiments show that our proposed method can has a stronger ability to suppress grating lobes and spatial aliasing than conventional methods in the estimation for wideband DOA and adapt to the scenarios where the number of sources also changes.

Sparse hydrophone array synthesis for frequency‐invariant and multi‐angle beampatterns using block‐structured Bayesian compressive sensing

“…Sensor arrays have been widely used in radar [1], sonar [2], remote sensing [3] and other systems [4,5], due to the significant advantages in terms of implementation cost and hardware complexity. Recently, the wideband beamforming capability has become an important indicator for sparse array design [6]. The conventional beamforming performance degrades in the wideband signal processing of sparse array, since the system response of the array changes with frequencies.…”

“…It means that the mainlobe width and sidelode levels of the array beampattern do not change with the frequency and steering angle. For uniform arrays, the wideband beamformers have been applied to practical signal processing [6,7]. However, sparse array design for wideband applications remains challenging due to the difficulty in controlling the shape of beampatterns.…”

Non-Circular Signal DOA Estimation with Nested Array via Off-Grid Sparse Bayesian Learning

“…However, the methods mentioned above only pay attention to the difference co-array of sparse arrays while ignoring the contribution of sum co-arrays. Non-circular (NC) signals [29,30] (such as binary phase shift keying (BPSK), pulse amplitude modulation (PAM), and amplitude shift keying (ASK)) can expand the array aperture and increase the DOFs, which are widely used in modern digital modulation schemes such as telecommunication or satellite systems [31][32][33]. The author in [34] converts the received model with NC signals into a real-valued sparse model and proposes a real-valued signal processing method in impulsive noise based on SBL.…”