12 accepted papers
We present a general Bernoulli Gaussian scale mixture based approach for modeling priors that can represent a large class of random signals. For inference, we introduce belief propagation (BP) to multi-snapshot signal recovery based on the minimum mean square error estimation criteria. Our method re…
This paper introduces new and practically relevant non-Gaussian priors for the Sparse Bayesian Learning (SBL) framework applied to the Multiple Measurement Vector (MMV) problem. We extend the Gaussian Scale Mixture (GSM) framework to model prior distributions for row vectors, exploring the use of sh…
We propose a distributed multisensor joint integrated probabilistic data association (JIPDA) filter for multiobject tracking in decentralized sensor networks. Conventional Chernoff fusion of the posterior multiobject distributions of neighboring sensors presupposes a correct "hard" association of th…
Passive acoustic monitoring (PAM) is a nonintrusive approach to studying behaviors of vocalizing marine organisms underwater that otherwise would remain unexplored. In this paper, we propose a data processing chain that can detect and track multiple whales in 3-D from passively recorded underwater a…
We present a sparsity-promoting method for the detection and estimation of the directions of arrival (DOAs) of source signals. The proposed method is based on the recently introduced variational Bayesian line spectral estimation (VALSE) approach, which is gridless. However, the performance of VALSE…
Cooperative localization is an enabling technology for the IoT that will introduce innovative services for modern convenience and public safety. Particle-based belief propagation (BP) is a state-of-the-art method for cooperative localization. However, in large and dense cooperative localization netw…
Estimating position and orientation change of a mobile platform from two consecutive point clouds provided by a high-resolution sensor is a key problem in autonomous navigation. In particular, scan matching algorithms aim to find the translation and rotation of the platform such that the two point c…
This paper presents a factor graph formulation and sum-product algorithm (SPA) for scalable detection and tracking of extended objects that generate multiple measurements. The proposed method dynamically introduces newly detected objects into the state space and efficiently performs probabilistic mu…
The sum-product algorithm (SPA) was recently shown to provide a scalable methodology for multitarget tracking (MTT) using multiple sensors. Here, we focus on another advantage of the SPA frame-work, namely, its capacity for Bayesian fusion of heterogeneous data sources and auxiliary information. We…
We propose a method for detecting and estimating multiple objects from multiple noisy images with partly overlapping observation areas. The goal is to detect the objects that are “locally” present in the individual observation areas and to estimate their states. Our method is based on a new closed-f…
This paper addresses the problem of localizing an unknown number of static sources emitting unknown signals from time-difference of arrival (TDOA) measurements. Based on the framework of random finite sets and finite set statistics, we formulate the Bayesian estimation problem and develop a particle…
We propose a Bayesian method for cooperative localization and control in mobile agent networks. Distributed, cooperative self-localization of each agent is supported by an information-seeking control of the movement of the agents. For cooperative localization, the SPAWN message passing scheme is use…