Particle filters are used extensively for tracking the state of non-linear dynamic systems. This paper presents a new particle filter that maintains samples in the state space at dynamically varying resolution for computational efficiency. Resolution within statespace varies by region, depending on the belief that the true state lies within each region. Where belief is strong, resolution is fin...

Particle Filter (PF) is the most widely used Bayesian sequential estimation method for obtaining hidden states of nonlinear dynamic systems. However, it still suffers from certain problems such as the loss of particle diversity, the need for large number of particles, and the costly selection of the importance density functions. In this paper, a novel PF called Exponential Natural Particle Filt...

In many systems the state variables are defined on a compact set of the state space. To estimate the states of such systems, the constrained particle filters have been used with some success. The performance of the standard particle filters can be improved if the measurement information is used during the importance sampling of the filtering phase. It has been shown that the particles obtained ...

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Particle filtering is an effective sequential Monte Carlo approach to solve the recursive Bayesian filtering problem in non-linear and non-Gaussian systems. The algorithm is based on importance sampling. However, in the literature, the proper choice of the proposal distribution for importance sampling remains a tough task and has not been resolved yet. Inspired by the animal swarm intelligence ...

in order to compare sampling efficiencies of total and inhalable dust methods, three airborne dust sampling systems , 7-hole sampler , close and open face filter cassette were compared side-by-side , as stationary and for the personal samplers , in a wood working industry to evaluate their relative efficiency. a total of 162 samples were collected. the study of particle size distribution by cas...

Particle filtering is an essential tool to improve uncertain model predictions by incorporating noisy observational data from complex systems including non-Gaussian features. A class of particle filters, clustered particle filters, is introduced for high-dimensional nonlinear systems, which uses relatively few particles compared with the standard particle filter. The clustered particle filter c...

This overview paper describes the particle methods developed for the implementation of the a class of Bayes filters formulated using the random finite set formalism. It is primarily intended for the readership already familiar with the particle methods in the context of the standard Bayes filter. The focus in on the Bernoulli particle filter, the probability hypothesis density (PHD) particle fi...