Particle filter for extracting target label information when targets move in close proximity

This paper addresses the problem of approximating the posterior probability density function of two targets after a crossing from the Bayesian perspective such that the information about target labels is not lost. To this end, we develop a particle filter that is able to maintain the inherent multimodality of the posterior after the targets have moved in close proximity. Having this approximation available, we are able to extract information about target labels even when the measurements do not provide information about target's identities. In addition, due to the structure of our particle filter, we are able to use an estimator that provides lower optimal subpattern assignment (OSPA) errors than usual estimators

Nonlinear filtering update phase via the Single Point Truncated Unscented Kalman filter

A fast algorithm to approximate the first two moments of the posterior probability density function (pdf) in nonlinear non-Gaussian Bayesian filtering is proposed. If the pdf of the measurement noise has a bounded support and the measurement function is continuous and bijective, we can use a modified prior pdf that meets Bayes' rule exactly. The central idea of this paper is that a Kalman filter applied to a modified prior distribution can improve the estimate given by the conventional Kalman filter. In practice, bounded support is not required and the modification of the prior is accounted for by adding an extra-point to the set of sigma-points used by the unscented Kalman filter

Estimating The dynamics of aberration components in the human eye

To provide adequate information that would assist surgeons in performing advanced refractive corrections, it is now essestial to address the problem of microfluctuations in the eye's aberrations due to pulse and respiration. Although the effects of fluctuations in defocus are known and well described, very little is reported on modelling the fluctuations in other types of aberrations. We propose a methodology in which the dynamics of higher order aberation components are modelled by parametric AM-FM signals. Using our modelling approach, the effects of changes in these aberrations could be predicted and studied. In particular, we model the dynamics of components related to coma and sperical aberration. We provide a validation of the proposed modelling approach using aberration data from the eyes of six subjects

Fundamentals of Object Tracking

Introduces object tracking algorithms from a unified, recursive Bayesian perspective, along with performance bounds and illustrative examples