Particle filter based on particle swarm optimization algorithm (PSO-PF) is not precise and trapping in local optimum easily, it is not able to satisfy the requirement of advanced integrated navigation system. In order to solve these problems, a novel particle filter algorithm based on dynamic neighborhood population adaptive particle swarm optimization (DPSO-PF) is presented in this paper. This...

Aims The aim of this tutorial is to introduce particle filters to those with a background in “classical” recursive estimation based on variants of the Kalman filter. We describe the principles behind the basic particle filter algorithm and provide a detailed worked example. We also show that the basic algorithm is a special case of a more general particle filter that greatly extends the filter ...

Particle filters are Monte Carlo methods that aim to approximate the optimal filter of a partially observed Markov chain. In this paper, we study the case in which the transition kernel of the Markov chain depends on unknown parameters: we construct a particle filter for the simultaneous estimation of the parameter and the partially observed Markov chain (adaptive estimation) and we prove the c...

Latent vector autoregressive models for categorical time series have a wide range of potential applications from marketing research to healthcare analytics. However, a bruteforce particle filter implementation of the Expectation-Maximization (EM) algorithm often fails to estimate the maximum likelihood parameters due to the Monte Carlo approximation of the E-step and multiple local optima of th...

Particle filters are an important class of online posterior density estimation algorithms. In this paper we propose a real coded genetic algorithm particle filter (RGAPF) for the dual estimation of stochastic volatility and parameters of a Heston type stochastic volatility model. We compare the performance of our hybrid particle filter with a parameter learning particle filter present in litera...

Particle filter is a probability estimation method based on Bayesian framework and it has unique advantage to describe the target tracking non-linear and non-Gaussian. In this study, firstly, analyses the particle degeneracy and sample impoverishment in particle filter multi-target tracking algorithm and secondly, it applies Markov Chain Monte Carlo (MCMC) method to improve re-sampling process ...

The lack of the latest measurement information and the Particle serious degradation cause low estimation precision in the tradition particle filter SLAM (simultaneous localization and mapping). For solve this problem, a SRCPF-SLAM (square cubature particle filter simultaneous localization and mapping) is proposed in this paper. The algorithm fuses the latest measurement information in the stage...

This paper concerns the problem of tracking acoustic sources in reverberant environments by using a particle filter. The localization problem is transformed into the retrieval of the unobservable state of a dynamical model through noisy measures. Though effective, two problems are related to particle filter: the degeneracy phenomenon (all particles but one are not significative) and the loss of...