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 ...

This paper proposes the use of a particle filter with embedded particle swarm optimization as an efficient and effective way of dealing with 3d model-based human body tracking. A particle swarm optimization algorithm is utilized in the particle filter to shift the particles toward more promising configurations of the human model. The algorithm is shown to be able of tracking full articulated bo...

Tracking a capsule endoscope location is one of promising application offered by implant body area networks (BANs). In this paper, we pay attention to a particle filter algorithm with received signal strength indicator (RSSI)-based localization in order to solve the capsule endoscope location tracking problem, which assumes a nonlinear transition model on the capsule endoscope location. However...

Leveraging information from the publicly accessible data repositories can be very useful when training a classifier from a small-sample microarray data. To achieve this, we proposed a multi-task feature selection filter that borrows strength from auxiliary microarray data. It uses Kruskal-Wallis test on auxiliary data and ranks genes based on their aggregated p-values. The top-ranked genes are ...

The focus of this paper is Bayesian state and parameter estimation using nonlinear models. A recently developed method, the particle filter, is studied that is based on stochastic simulation. Unlike the well-known extended Kalman filter, the particle filter is applicable to highly nonlinear models with non-Gaussian uncertainties. Recently developed techniques that improve the convergence of the...

This paper presents a Rao-Blackwellized mixed state particle filter for joint head tracking and pose estimation. Rao-Blackwellizing a particle filter consists of marginalizing some of the variables of the state space in order to exactly compute their posterior probability density function. Marginalizing variables reduces the dimension of the configuration space and makes the particle filter mor...

Visual object tracking is the active area of research in the computer vision. Various techniques have being developed for the object tracking .Particle filter deals with the tracking of the objects having non linear motion .Particle filter has been used in for various fields and application efficiently. Proposed Technique is based on combination of kalman filter, mean shift and particle filter....

In this paper, we propose a new nonlinear filtering algorithm that can provide more accurate and reliable localization compared with the pure particle filtering (PF). In the proposed algorithm, failures of the PF are detected, and the failed PF is recovered using a finite impulse response (FIR) filter. The resulting filter is called the combined particle/FIR filter (CPFF). We demonstrate the pe...