have the risk of collapse where the assumption of Gaussian distribution is not applicable. It is well known that state estimation with a particle filter is very robust against clutter in dynamic and noisy environments because of its ability to represent non-Gaussian distributions. Unfortunately

ABSTRACT We propose a framework for optimization problems based on particle filtering (also called Sequential Monte Carlo method). This framework unifies and provides new insight into randomized optimization algorithms. The framework also sheds light on developing new optimization algorithms

One of the key motivating factors for using particle filters for prognostics is the ability to include model parameters as part of the state vector to be estimated. This performs model adaptation in conjunction with state tracking, and thus, produces a tuned model that can used for long term

We propose a framework for optimization problems based on particle filtering (also called Sequential Monte Carlo method). This framework unifies and provides new insight into randomized optimization algorithms. The framework also sheds light on developing new optimization algorithms through

In this paper, we present a work-in-progress toward multiple object tracking exploiting the GPU as the main processor. This work is based on new Shader Model 3.0 capabilities and recent research on GPU tracking, such as [Montemayor et al. 2006], extending the

propose to estimate or update the linear prediction matrix directly on the noisy speech observations. This is possible within the particle filter framework by weighting the different noisy estimates (particles) due to their likelihood in the estimation equation of the linear prediction matrix. Speech

This paper formulates the problem of real-time estimation of traffic state in freeway networks by means of the particle filtering framework. A particle filter (PF) is developed based on a recently proposed speed-extended cell-transmission model of freeway traffic. The freeway is considered as a

distribution concentrated on the global optimum. Hence, the goal of searching for the global optimum can be achieved by computing the conditional distribution. Since this computation is often analytically intractable, we approximate it by particle filtering, a class of sequential Monte Carlo methods

filtering and provides a robust framework for long-term prognosis while accounting effectively for uncertainties. Correction terms are estimated in a learning paradigm to improve the accuracy and precision of the algorithm for long-term prediction. The proposed approach is applied to a crack fault

space (deformations). Recently, the incorporation of the particle filter with geometric active contours which use first and second moments has shown robust tracking results. By generalizing the statistical inference to entire probability distributions, we introduce a new distribution metric for tracking