Acoustic DOA estimation using space alternating sparse Bayesian learning

Estimating the direction-of-arrival (DOA) of multiple acoustic sources is one of the key technologies for humanoid robots and drones. However, it is a most challenging problem due to a number of factors, including the platform size which puts a constraint on the array aperture. To overcome this problem, a high-resolution DOA estimation algorithm based on sparse Bayesian learning is proposed in this paper. A group sparse prior based hierarchical Bayesian model is introduced to encourage spatial sparsity of acoustic sources. To obtain approximate posteriors of the hidden variables, a variational Bayesian approach is proposed. Moreover, to reduce the computational complexity, the space alternating approach is applied to push the variational Bayesian inference to the scalar level. Furthermore, an acoustic DOA estimator is proposed to jointly utilize the estimated source signals from all frequency bins. Compared to state-of-the-art approaches, the high-resolution performance of the proposed approach is demonstrated in experiments with both synthetic and real data. The experiments show that the proposed approach achieves lower root mean square error (RMSE), false alert (FA), and miss-detection (MD) than other methods. Therefore, the proposed approach can be applied to some applications such as humanoid robots and drones to improve the resolution performance for acoustic DOA estimation especially when the size of the array aperture is constrained by the platform, preventing the use of traditional methods to resolve multiple sources.