Although particle filter and its variants like KPF and UPF achieve great success in many visual tracking applications, they depend on local proposal distributions and hence always fall flat in cases of global object shift and large-scale object movement. To address this issue, we present the concept of global proposal distribution for particle filter with the inspiration from biological vision ...

The penetration result obtained on a filter test stand depends on several factors. Two of these are challenge aerosol size distribution and the particle detector used to measure the particle size and concentration. Some filter testers employ a monodisperse aerosol challenge while others use a polydisperse challenge. Some particle detectors are based on particle counting while others are based o...

Optical sensors based vehicle tracking can be widely implemented in traffic surveillance and flow control. The vast development of video surveillance infrastructure in recent years has drawn the current research focus towards vehicle tracking using high-end and low cost optical sensors. However, tracking vehicles via such sensors could be challenging due to the high probability of changing vehi...

In the present study, a theoretical model was used to examine factors affecting the filtration characteristics of filters used for respiratory protection. This work was designed to support the particulate filter test requirements established in 1996. The major operating parameters examined in this work include face velocity, fiber diameter, packing density, filter thickness, and fiber charge de...

Recursively estimating the likelihood of a set of parameters, given a series of observations, is a common problem in signal processing. The Kalman filter is a The particle filter is now a well-known alternative to the Kalman filter. It represents the likelihood as a set of samples with associated weights and so can approximate any distribution. It can be applied to problems where the process mo...

As an approximation of the optimal stochastic filter, particle filter is a widely used tool for numerical prediction of complex systems when observation data are available. In this paper, we conduct an error analysis from a numerical analysis perspective. That is, we investigate the numerical error, which is defined as the difference between the numerical implementation of particle filter and i...

The particle filter is a Bayesian estimation technique based on Monte Carlo simulation. The nonparametric nature of particle filters makes them ideal for non-linear, non-Gaussian dynamic systems. Particle filtering has many applications: in computer vision, robotics, and econometrics to name just a few. Although superior to Kalman filters, particle filters have higher computational requirements...

In this study, an unscented particle filtering method based on an interacting multiple model (IMM) frame for a Markovian switching system is presented. The method integrates the multiple model (MM) filter with an unscented particle filter (UPF) by an interaction step at the beginning. The framework (interaction/mixing, filtering, and combination) is similar to that in a standard IMM filter, but...