Abstract

Page 1. Multi-frequency sparse Bayesian learning with noise models Kay L. Gemba1, Santosh Nannuru, and Peter Gerstoft ASA SD FALL 2019 Marine Physical Laboratory of the Scripps Institution of Oceanography University of California at San Diego 1gemba@ucsd.edu 1 Page 2. Presentation objectives We investigate SBL performance and present results for the MFP and beamforming applications using 3 noise models: 1. SBL behaves similarly to an adaptive processor and displays robustness to modest model-data mismatch. SBL performance is compared to the white noise gain constraint (WNGC), MUSIC, and Bartlett processors. 2. Compare SBL performance, implemented with 3 noise models: 1. SBL1: stationary noise, 2. SBL2: non-stationary noise, which is useful when the noise variance evolves across snapshots, 3. SBL3: non-stationary noise in a spatially, non-homogeneous field (example: surface noise) …

Abstract

The paper considers direction of arrival (DOA) estimation from long-term observations in a noisy environment. In such an environment the noise source might evolve, causing the stationary models to fail. Therefore a heteroscedastic Gaussian noise model is introduced where the variance can vary across observations and sensors. The source amplitudes are assumed independent zero-mean complex Gaussian distributed with unknown variances (i.e. the source powers), leading to stochastic maximum likelihood (ML) DOA estimation. The DOAs of plane waves are estimated from multi-snapshot sensor array data using sparse Bayesian learning (SBL) where the noise is estimated across both sensors and snapshots. Simulations demonstrate that taking the heteroscedastic noise into account improves DOA estimation.

Abstract

Sparse Bayesian learning (SBL) has been used to obtain source direction-of-arrivals (DoAs) from uniform linear array (ULA) data. The maximum number of sources that can be resolved using a ULA is limited by the number of sensors in the array. It is known that sparse linear arrays such as co-prime and nested arrays can resolve more sources than the number of sensors. In this paper we demonstrate this using SBL. We compute the mean squared error in source power estimation as various parameters are varied.

Abstract

Using simulations and data, we localize a quiet source in the presence of an interferer. The SWellEx-96 Event S59 consists of a submerged source towed along an isobath over a 65 min duration with an interferer traversing the source track. This range independent, multi-frequency scenario includes mismatch, non-stationary noise, and operational uncertainty. Mismatch is defined as a misalignment between the actual source field observed at the array and the modeled replica vector. The noise process changes likely with time. This is modelled as a heteroscedastic Gaussian process, meaning that the noise variance is non-stationary across snapshots. Sparse Bayesian learning (SBL) has been applied previously to the matched field processing application [Gemba et al, J. Acoust. Soc. Am., 141:3411-3420, 2017]. Results demonstrate that SBL exhibits desirable robustness properties and improved localization …