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 …

The paper presents two algorithms for Decentralized Bayesian information fusion and
information-theoretic decision making. The algorithms are stated in terms of operations on a …

This paper presents a decentralised particle filtering algorithm that enables multiple vehicles
to jointly track 3D features under limited communication bandwidth. This algorithm, applied …

The paper presents a decentralized approach to the solution of the distributed information
gathering problem. The main design objectives are scalability with the number of network …

spatially sparse received signal strength measurements obtained with multiple devices.
First, we show that the residual of the linear regression between devices, usually …

This paper presents two solutions for performing decentralised particle filtering in view of
non-linear, non-Gaussian tracking in sensor networks. The issue is that no known methods …

We propose an estimation algorithm for stochastic linear hybrid systems with continuous-
state-dependent mode transitions. We utilize Gaussian mixture approximations to overcome …

This paper focuses on a decentralised nonlinear estimation problem in a multiple sensor
network. The stress is laid on the optimal fusion of probability densities conditioned by …

This paper compares and constrasts three different probabilistic models-Particle
representations, Parzen density estimates, and Gaussian mixture models-for non-Gaussian …

This work examines the problem of using finite Gaussian mixtures (GMs) in Bayesian
message passing algorithms for decentralized data fusion (DDF). It is shown that both exact …