This work presents a constrained abridged Gaussian sum extended Kalman filter
(constrained AGS–EKF) that employs Gaussian mixture models to improve the estimation of …

Distributed data fusion (DDF) is the process whereby a group of agents sense their local
environment, communicate with other agents, and collectively try to infer knowledge about a …

A novel probabilistic perception algorithm is presented as a real-time joint solution to data
association, object tracking, and object classification for an autonomous ground vehicle in …

A novel approach to utilize negative information to improve the precision and accuracy of
extended multiobject tracking is presented. The parameterized probability density of object …

A new method has been developed to approximate one Gaussian sum by another. This
algorithm is being developed as part of an effort to generalize the concept of a particle filter …

This article develops novel strategies for optimal planning with semantic observations using
continuous state partially observable Markov decision processes (CPOMDPs). Two major …

For linear dynamic systems with Gaussian noise, the Kalman filter provides the Minimum
Mean-Square Error (MMSE) state estimation by tracking the posterior. Similarly, for systems …

In this work, we propose an approximate minimum mean-square error filter for linear
dynamic systems with Gaussian Mixture (GM) noise. The proposed estimator tracks each …

We consider the problem of evaluating dynamic consistency in discrete time probabilistic
filters that approximate stochastic system state densities with Gaussian mixtures. Dynamic …

This paper presents a novel nonparametric approach toward delayed-state filtering for
cooperative tracking. Standard parametric cooperative localization/tracking approaches are …