Fully Bayesian Wideband Direction-of-Arrival Estimation and Detection via RJMCMC
Kyurae Kim, Philip T. Clemson, James P. Reilly, Jason F. Ralph, Simon, Maskell

TL;DR
This paper introduces a fully Bayesian method for wideband DoA estimation and detection using NRJMCMC, modeling signals in the time domain and exploiting system structure for computational efficiency.
Contribution
It presents a novel Bayesian framework with closed-form marginalization and a computationally efficient RJMCMC approach for wideband DoA estimation and detection.
Findings
Achieves lower autocorrelation and faster convergence with NRJMCMC
Reduces likelihood computation complexity from O(N^3 k^3) to O(N k^3)
Demonstrates competitive detection performance against GLRT and information criteria
Abstract
We propose a fully Bayesian approach to wideband, or broadband, direction-of-arrival (DoA) estimation and signal detection. Unlike previous works in wideband DoA estimation and detection, where the signals were modeled in the time-frequency domain, we directly model the time-domain representation and treat the non-causal part of the source signal as latent variables. Furthermore, our Bayesian model allows for closed-form marginalization of the latent source signals by leveraging conjugacy. To further speed up computation, we exploit the sparse ``stripe matrix structure'' of the considered system, which stems from the circulant matrix representation of linear time-invariant (LTI) systems. This drastically reduces the time complexity of computing the likelihood from to , where is the number of samples received by the array and is the…
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Taxonomy
TopicsDirection-of-Arrival Estimation Techniques · Speech and Audio Processing · Antenna Design and Optimization
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
