Multilateration aircraft tracking using stochastic filtering techniques

Tracking Multiple Moving Objects Using Unscented Kalman Filtering Techniques

It is an important task to reliably detect and track multiple moving objects for video surveillance and monitoring. However, when occlusion occurs in nonlinear motion scenarios, many existing methods often fail to continuously track multiple moving objects of interest. In this paper we propose an effective approach for detection and tracking of multiple moving objects with occlusion. Moving tar...

Object Visual Tracking using Window-Matching Techniques and Kalman Filtering

Object visual tracking aims to determine the image configuration of a target region of an object as it moves through a camera's field of view. The visual tracking process consists on matching the target region in successive frames of a sequence of images taken at closelyspaced intervals. Visual tracking has become an important process on various applications as: vision-based control (Hutchinson...

Probabilistic Analysis of Wake Vortex Hazards for Landing Aircraft Using Multilateration Data

Wake vortices are a safety hazard to landing aircraft. A landing aircraft that encounters a wake may roll, resulting in a fatal crash if the roll is severe enough and/or if the aircraft is low enough to the ground. Therefore, aircraft are separated by a sufficient distance to ensure that wakes have time to decay in strength. However, because of the inherent variability in wake behavior and airc...

A stochastic filtering technique for fluid flows velocity fields tracking

In this paper we present a method for the temporal tracking of fluid flows velocity fields. The technique we propose is formalized within a sequential Bayesian filtering framework. The filtering model combines an Itô diffusion process coming from a stochastic formulation of the vorticity-velocity form of the Navier-Stokes equation and discrete measurements extracted from the image sequence. In ...

Application of Post Kalman Filtering Techniques to Tracking Problems