@inproceedings{bib_SPAR_2021, AUTHOR = {Ruchi Pandey, Santosh Nannuru, Aditya Siripuram}, TITLE = {SPARSE BAYESIAN LEARNING FOR ACOUSTIC SOURCE LOCALIZATION}, BOOKTITLE = {International Conference on Acoustics, Speech, and Signal Processing}. YEAR = {2021}}

The localization of acoustic sources is a parameter estimation problem where the parameters of interest are the direction of arrivals (DOAs). The DOA estimation problem can be formulated as a sparse parameter estimation problem and solved using compressive sensing (CS) methods. In this paper, the CS method of sparse Bayesian learning (SBL) is used to find the DOAs. We specifically use multi-frequency SBL leading to a non-convex optimization problem, which is solved using fixed-point iterations. We evaluate SBL along with traditional DOA estimation methods of conventional beamforming (CBF) and multiple signal classification (MUSIC) on various source localization tasks from the open access LOCATA dataset. The comparative study shows that SBL significantly outperforms CBF and MUSIC on all the considered tasks.

@inproceedings{bib_Spar_2021, AUTHOR = {Santosh Nannuru, Peter Gerstoft, Guoli Ping, Efren Fernandez-Grande}, TITLE = {Sparse planar arrays for azimuth and elevation using experimental data}, BOOKTITLE = {The Journal of the Acoustical Society of America}. YEAR = {2021}}

Sparse arrays are special geometrical arrangements of sensors which overcome some of the drawbacks associated with dense uniform arrays and require fewer sensors. For direction finding applications, sparse arrays with the same number of sensors can resolve more sources while providing higher resolution than a dense uniform array. This has been verified numerically and with real data for one-dimensional microphone arrays. In this study the use of nested and co-prime arrays is examined with sparse Bayesian learning (SBL), which is a compressive sensing algorithm, for estimating sparse vectors and support. SBL is an iterative parameter estimation method and can process multiple snapshots as well as multiple frequency data within its Bayesian framework. A multi-frequency variant of SBL is proposed, which accounts for non-flat frequency spectra of the sources. Experimental validation of azimuth and elevation [two-dimensional (2D)] direction-of-arrival (DOA)estimation are provided using sparse arrays and real data acquired in an anechoic chamber with a rectangular array. Both co-prime and nested arrays are obtained by sampling this rectangular array. The SBL method is compared with conventional beamforming and multiple signal classification for 2D DOA estimation of experimental data.

@inproceedings{bib_ENER_2020, AUTHOR = {Jie ZHANG, Prof. Dr. Ir. R. Heusdens, Dr. Ir. R. C. Hendrik, Prof. Dr. J. Jensen, Ing. T. Gerkmann, ir. A. Bertrand, . Ir. G.J.T. Leus, A. Hanjalic, Santosh Nannuru}, TITLE = {ENERGY-AWARE NOISE REDUCTION FOR WIRELESS ACOUSTIC SENSOR NETWORKS}, BOOKTITLE = {Frontiers in Neurology}. YEAR = {2020}}

This paper develops efficient channel estimation techniques for millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems under practical hardware limitations, including an arbitrary array geometry and a hybrid hardware structure. Taking on an angle-based approach, this work adopts a generalized array manifold separation approach via Jacobi-Anger approximation, which transforms a non-ideal, non-uniform array manifold into a virtual array domain with a desired uniform geometric structure to facilitate super-resolution angle estimation and channel acquisition. Accordingly, structure-based optimization techniques are developed to effectively estimate both the channel covariance and the instantaneous channel state information (CSI) within a short sensing time. The different time-varying scales of channel path angles versus path gains are capitalized to design a two-step CSI estimation scheme that can quickly sense fading channels. Theoretical results are provided on the fundamental limits of the proposed technique in terms of sample efficiency. For computational efficiency, a fast iterative algorithm is developed via the alternating direction method of multipliers. Other related issues such as spurious-peak cancellation in non-uniform linear arrays and extensions to higher-dimensional cases are also discussed. Simulations testify the effectiveness of the proposed approaches in hybrid mmWave massive MIMO systems with arbitrary arrays.

@inproceedings{bib_Mult_2019, AUTHOR = {Kay L. Gemba, Santosh Nannuru, Peter Gerstoft}, TITLE = {Multi-frequency sparse Bayesian learning with noise models}, BOOKTITLE = {The Journal of the Acoustical Society of America}. YEAR = {2019}}

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) …

@inproceedings{bib_DOA__2019, AUTHOR = {Peter Gerstoft, Santosh Nannuru, Christoph F. Mecklenbräuker, Geert Leus }, TITLE = {DOA Estimation in heteroscedastic noise}, BOOKTITLE = {Signal Processing}. YEAR = {2019}}

The paper considers direction of arrival (DOA) estimation from long-term observations in a very noisy environment. The concern is to derive methods obtaining reasonable DOAs at very low SNR. The noise is assumed zero-mean Gaussian and its variance varies in time and space, causing stationary data models to fit poorly over long observation times. Therefore a heteroscedastic Gaussian noise model is introduced where the variance varies across observations and sensors. The source amplitudes are assumed independent zero-mean complex Gaussian distributed with unknown variances (i.e. the source powers), inspiring 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. This SBL approach is more flexible and performs better …

@inproceedings{bib_Spar_2019, AUTHOR = {Santosh Nannuru, Kay L. Gemba, Peter Gerstof, William S. Hodgkiss, Christoph Mecklenbrauker}, TITLE = {Sparse Bayesian learning with multiple dictionaries}, BOOKTITLE = {Signal Processing}. YEAR = {2019}}

Sparse Bayesian learning (SBL) has emerged as a fast and competitive method to perform sparse processing. The SBL algorithm, which is developed using a Bayesian framework, iteratively solves a non-convex optimization problem using fixed point updates. It provides comparable performance and is significantly faster than convex optimization techniques used in sparse processing. We propose a multi-dictionary SBL algorithm that simultaneously can process observations generated by different underlying dictionaries sharing the same sparsity profile. Two algorithms are proposed and corresponding fixed point update equations are derived. Noise variances are estimated using stochastic maximum likelihood. The multi-dictionary SBL has many practical applications. We demonstrate this using direction-of-arrival (DOA) estimation. The first example uses the proposed multi-dictionary SBL to process multi …

@inproceedings{bib_2D_b_2019, AUTHOR = {Santosh Nannuru, Peter Gerstof}, TITLE = {2D beamforming on sparse arrays with sparse Bayesian learning}, BOOKTITLE = {International Conference on Acoustics, Speech, and Signal Processing}. YEAR = {2019}}

Sparse arrays such as co-prime and nested arrays can identify more sources than the number of sensors. This is because their difference co-arrays contain a uniformly spaced virtual array with more elements than the number of sensors in the array. In this paper we demonstrate this using two dimensional co-prime and nested sparse arrays combined with sparse Bayesian learning (SBL) for 2D beamforming in azimuth and elevation. SBL can directly process the sparse array data and significantly outperform conventional beam-forming and MUSIC as seen from simulations.

@inproceedings{bib_Mult_2018, AUTHOR = {Kay L Gemba, Santosh Nannuru, Peter Gerstoft}, TITLE = {Multi-frequency sparse Bayesian learning for matched field processing in non-stationary noise}, BOOKTITLE = {The Journal of the Acoustical Society of America}. YEAR = {2018}}

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 …

@inproceedings{bib_Spar_2018, AUTHOR = {Santosh Nannuru, Peter Gerstof, Ali Koochakzadeh, Piya Pal}, TITLE = {Sparse Bayesian learning for DOA estimation using co-prime and nested arrays}, BOOKTITLE = {Sensor Array and Multichannel Signal Processing Workshop}. YEAR = {2018}}

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.

@inproceedings{bib_DOA__2018, AUTHOR = {Peter Gerstof, Santosh Nannuru, Christoph F. Mecklenbrauker, Geert Leus}, TITLE = {DOA ESTIMATION IN HETEROSCEDASTIC NOISE WITH SPARSE BAYESIAN LEARNING}, BOOKTITLE = {International Conference on Acoustics, Speech, and Signal Processing}. YEAR = {2018}}

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.