Underwater robots play a critical role in the marine industry. Object perception is the
foundation for the automatic operations of submerged vehicles in dynamic aquatic …

We propose an online multi-target tracker that exploits both high-and low-confidence target
detections in a Probability Hypothesis Density Particle Filter framework. High-confidence …

The theoretically optimal approach to multisensor-multitarget detection, tracking, and
identification is a suitable generalization of the recursive Bayes nonlinear filter. Even in …

A new recursive algorithm is proposed for jointly estimating the time-varying number of
targets and their states from a sequence of observation sets in the presence of data …

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 …

The multitarget recursive Bayes nonlinear filter is the theoretically optimal approach to
multisensor-multitarget detection, tracking, and identification. For applications in which this …

The Gaussian mixture probability hypothesis density (GM-PHD) recursion is a closed-form
solution to the probability hypothesis density (PHD) recursion, which was proposed for …

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 …

The probability hypothesis density (PHD) filter is an attractive approach to tracking an
unknown and time-varying number of targets in the presence of data association uncertainty …