Wireless sensor networks produce a large amount of data that needs to be processed,
delivered, and assessed according to the application objectives. The way these data are …

Random finite sets (RFSs) are natural representations of multitarget states and observations
that allow multisensor multitarget filtering to fit in the unifying random set framework for data …

We present Monte Carlo methods for multi-target tracking and data association. The
methods are applicable to general nonlinear and non-Gaussian models for the target …

Particle methods are a set of powerful and versatile simulation-based methods to perform
optimal state estimation in nonlinear non-Gaussian state-space models. The ability to …

In this article we propose a new Rao-Blackwellized particle filtering based algorithm for
tracking an unknown number of targets. The algorithm is based on formulating probabilistic …

Multi-armed bandit (MAB) problems are a class of sequential resource allocation problems
concerned with allocating one or more resources among several alternative (competing) …

This work addresses the problem of tracking multiple moving targets by recursively
estimating the joint multitarget probability density (JMPD). Estimation of the JMPD is done in …

This paper concerns the sequential estimation of a hidden state vector from noisy
observations delivered by several sensors. Different from the standard framework, we …

In multi-object stochastic systems, the issue of sensor management is a theoretically and
computationally challenging problem. In this paper, we present a novel random finite set …

We propose a filtering framework for multitarget tracking that is based on the probability
hypothesis density (PHD) filter and data association using graph matching. This framework …