Motion correspondence is a fundamental problem in computer vision and many other
disciplines. This article describes statistical data association techniques originally …

The Interacting Multiple Model (IMM) estimator is a suboptimal hybrid filter that has been
shown to be one of the most cost-effective hybrid state estimation schemes. The main …

Existing multiple-model (MM) estimation algorithms have a fixed structure, ie they use a fixed
set of models. An important fact that has been overlooked for a long time is how the …

In tracking targets with less-than-unity probability of detection in the presence of false alarms
(FAs), data association-deciding which of the received multiple measurements to use to …

The probability hypothesis density (PHD) filter is an attractive approach to tracking an
unknown and time-varying number of targets in the presence of data association uncertainty …

A unique and comprehensive collection of submarine landslide data from different tectonic
environments in the North Atlantic, based on published literature and industry sources, is …

When there is no feedback from the fusion center to local sensors, we present a distributed
Kalman filtering fusion formula for linear dynamic systems with sensor noises cross …

Tracking multiple targets with uncertain target dynamics is a difficult problem, especially with
nonlinear state and/or measurement equations. With multiple targets, representing the full …

An exact solution is provided for the multiple sensor bias estimation problem based on local
tracks. It is shown that the sensor bias estimates can be obtained dynamically using the …

For the problem of tracking multiple targets, the joint probabilistic data association (JPDA)
approach has shown to be very effective in handling clutter and missed detections. The …