<p style='text-indent:20px;'>The purpose of this paper is to describe the feedback particle filter algorithm for problems where there are a large number (<inline-formula><tex-math id="M1">\begin{document}$ M $\end{document}</tex-math></inline-formula>) non-interacting agents (targets) with id="M2">\begin{document}$ non-agent specific observations (measurements) ...

This paper presents the Optimized Kalman Particle Swarm (OKPS) filter. This filter results from two years of research and improves the Swarm Particle Filter (SPF). The OKPS has been designed to be both cooperative and reactive. It combines the advantages of the Particle Filter (PF) and the metaheuristic Particle Swarm Optimization (PSO) for ego-vehicles localization applications. In addition to...

—In the computer vision community, the Condensation algorithm has received considerable attention. Recently, it has been proven that the algorithm is one variant of particle filter (also known as sequential Monte Carlo filter, sequential importance sampling etc.). In sampling stage of Condensation, particles are drawn from the prior probability distribution of the state evolution transition, wi...

We consider the analysis of data under mixture models where the number of components in the mixture is unknown. We concentrate on mixture Dirichlet process models, and in particular we consider such models under conjugate priors. This conjugacy enables us to integrate out many of the parameters in the model, and to discretize the posterior distribution. Particle filters are particularly well su...

Summary Sequential Monte Carlo methods are typically not straightforward to implement on parallel architectures. This is because standard resampling schemes involve communication between all particles. The $$\alpha$$-sequential method was proposed recently as a potential solution this that limits limited controlled through sequence of stochastic matrices known $$\alpha$$ matrices. We study the ...