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.