The problem of the optimal allocation (in expected mean square error sense) a measurement budget for particle filtering is addressed. We propose three different intermittent filters, whose optimality criteria depend on information available at time decision making. For first, stochastic program filter, times are given by policy that determines whether should be taken based measurements already ...

The particle filter is an effective image denoising technique. An important issue with the application of the particle filter is the selection of the filter parameters, which affect the results significantly. There are two main contributions of this paper. The first contribution is an estimation of the noise level. The second contribution is an improved particle filter (Rao-Blackwellized Partic...

This paper proposes a new radar tracking filter named Noise-estimate Particle Filter (NPF). Kalman filter and particle filter are popular filtering techniques for target tracking. The tracking performance of the Kalman filter severely depends on the setting of several parameters such as system noise and observation noise. However, it is an open problem how to choose proper parameters for variou...

The marginalized particle filter is a powerful combination of the particle filter and the Kalman filter, which can be used when the underlying model contains a linear sub-structure, subject to Gaussian noise. This paper outlines the marginalized particle filter and very briefly hint at possible generalizations, giving rise to a larger family of marginalized nonlinear filters. Furthermore, we an...

The probability hypothesis density (PHD) filter suffers from lack of precise estimation of the expected number of targets. The Cardinalized PHD (CPHD) recursion, as a generalization of the PHD recursion, remedies this flaw and simultaneously propagates the intensity function and the posterior cardinality distribution. While there are a few new approaches to enhance the Sequential Monte Carlo (S...

Particle filter algorithm is a filtering method which uses Monte Carlo idea within the framework of Bayesian estimation theory. It approximates the probability distribution by using particles and discrete random measure which is consisted of their weights, it updates new discrete random measure recursively according to the algorithm. When the sample is large enough, the discrete random measure ...