Context is widely regarded as a major determinant of learning and memory across
numerous domains, including classical and instrumental conditioning, episodic memory …

The general solution for dynamic state estimation is to model the system as a hidden Markov
process and then employ a recursive estimator of the prediction–correction format (of which …

Advanced multi-sensor systems are expected to combat the challenges that arise in object
recognition and state estimation in harsh environments with poor or even no prior …

This paper proposes a new Bayesian tracking and parameter learning algorithm for non-
linear and non-Gaussian multiple target tracking (MTT) models. A Markov chain Monte Carlo …

Multisensor state-space models underpin fusion applications in networks of sensors.
Estimation of latent parameters in these models has the potential to provide highly desirable …

Learning the model parameters of a multiobject dynamical system from partial and perturbed
observations is a challenging task. Despite recent numerical advancements in learning …

A new Bayesian state and parameter learning algorithm for multiple target tracking models
with image observations are proposed. Specifically, a Markov chain Monte Carlo algorithm …

Tracking space objects is important for managing space traffic and predicting collisions, but
is difficult in part due to data association and orbit model uncertainty. Expectation …

This paper investigates the problem of using a large number of independent, identical
sensors jointly for multi-object detection and estimation (MODE), namely massive sensor …

Multiple hypothesis tracking (MHT) is a computational procedure for recursively estimating
multi-object configurations and states from measurements with association uncertainties …