Localization of DOA trajectories -- Beyond the grid

The direction of arrival (DOA) estimation algorithms are crucial in localizing acoustic sources. Traditional localization methods rely on block-level processing to extract the directional information from multiple measurements processed together. However, these methods assume that DOA remains constant throughout the block, which may not be true in practical scenarios. Also, the performance of localization methods is limited when the true parameters do not lie on the parameter search grid. In this paper we propose two trajectory models, namely the polynomial and bandlimited trajectory models, to capture the DOA dynamics. To estimate trajectory parameters, we adopt two gridless algorithms: i) Sliding Frank-Wolfe (SFW), which solves the Beurling LASSO problem and ii) Newtonized Orthogonal Matching Pursuit (NOMP), which improves over OMP using cyclic refinement. Furthermore, we extend our analysis to include wideband processing. The simulation results indicate that the proposed trajectory localization algorithms exhibit improved performance compared to grid-based methods in terms of resolution, robustness to noise, and computational efficiency

Improving trajectory localization accuracy via direction-of-arrival derivative estimation

Sound source localization is crucial in acoustic sensing and monitoring-related applications. In this paper, we do a comprehensive analysis of improvement in sound source localization by combining the direction of arrivals (DOAs) with their derivatives which quantify the changes in the positions of sources over time. This study uses the SALSA-Lite feature with a convolutional recurrent neural network (CRNN) model for predicting DOAs and their first-order derivatives. An update rule is introduced to combine the predicted DOAs with the estimated derivatives to obtain the final DOAs. The experimental validation is done using TAU-NIGENS Spatial Sound Events (TNSSE) 2021 dataset. We compare the performance of the networks predicting DOAs with derivative vs. the one predicting only the DOAs at low SNR levels. The results show that combining the derivatives with the DOAs improves the localization accuracy of moving sources

Multitarget multisensor tracking

In this thesis we develop various multitarget tracking algorithms that can process measurements from single or multiple sensors. The filters are derived by approximate application of the recursive Bayes filter within the random finite set framework, which is used to model the multitarget state and observations. The contributions of the thesis can be organized into three main categories.To provide a motivating application for the algorithms we develop, we first study the problem of radio frequency tomography. We empirically validate a radio frequency tomography measurement model when multiple targets are present within the sensor network. We validate modelsfor both indoor and outdoor environments. These models are then used to perform multitarget tracking using various Monte Carlo filters on data gathered from field deployments of radio frequency sensor networks.Second, we develop auxiliary particle filter implementations of the Probability Hypothesis Density filter and Cardinalized Probability Hypothesis Density filter when the measurement model has a specific form, namely the superpositional sensor model. We also derive Multi-Bernoulli filter and Hybrid Multi-Bernoulli Cardinalized Probability Hypothesis Density filter for superpositional sensors and develop their auxiliary particle filter implementations. These filters are evaluated for multitarget tracking using simulated radio frequency tomography and acoustic sensor network models.Third, we derive update equations for the General Multisensor Cardinalized Probability Hypothesis Density filter when the measurement model has a specific form, namely the standard sensor model. To overcome the combinatorial computational complexity of this filter we develop a Gaussian mixture model-based greedy algorithmto implement the filter in a computationally tractable manner. The filter is evaluated using simulated multisensor measurements.Dans cette thèse nous développons différents algorithmes de pistage multicible qui peuvent traiter les mesures d'un ou plusieurs capteurs. Les filtres sont obtenus par application approximative du filtre de Bayes récursif dans le contexte d'ensemble fini aléatoire, contexte qui est utilisé pour modélisé les états et les observations. Les contributions de la thèse peuvent être organisées en trois parties.Pour fournir une application motivante des algorithmes que nous développons, nous étudions d'abord le problème de tomographie à radiofréquence. Nous validons empiriquement un modèle de mesure pour tomographie à radiofréquence lorsque plusieurs cibles sont présentes à l'intérieur du réseau de capteurs. Nous validons des modèles pour des environnements à la fois intérieurs et extérieurs. Ces modèles sont ensuite utilisés pour réaliser du pistage multicible utilisant différents filtres de Monte Carlo sur les données capturées lors de déploiements sur le terrain de réseaux de capteurs sans-fil.En second lieu, nous développons des implémentations de filtres particulaires auxiliaires pour le filtre ``Probability Hypothesis Density'' et le filtre ``Cardinalized Probability Hypothesis Density'' lorsque le modèle de mesure possède une forme particulière, à savoir le modèle superposé de capteur. Nous obtenons aussi un filtre multi-Bernouilli et un filtre ``Hybrid Multi-Bernoulli Cardinalized Probability Hypothesis Density'' pour les capteurs de modèle superposé et développons leurs implémentations de filtres particulaires auxiliaires. Ces filtres sont évalués à des fins de pistage multicible en utilisant de la tomographie à radiofréquence simulée et plusieurs modèles acoustiques de réseaux de capteurs.En troisième lieu, nous dérivons des équations de mise-à-jour pour le filtre ``General Multisensor Cardinalized Probability Hypothesis Density'' lorsque le modèle de mesure possède une forme particulière, à savoir le modèle standard de capteur. Pour surmonter la complexité combinatoire de ce filtre, nous développons un algorithme glouton avec mélange de gaussiennes qui est effectivement traitable en temps fini. Le filtre est évalué en utilisant des mesures simulées provenant de différents capteurs

Sparse Bayesian Learning for Acoustic Source Localization

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. © 2021 IEE