Decentralised Data Fusion with Particles

Data Fusion in Decentralised Sensing Networks

This paper briefly describes the results of a ten year, and still on-going, research program in decentralised sensing systems. This program covers both the theoretical development of data fusion methods appropriate to networks of decentralised sensors and the practical implementation of these in both civilian and military contexts. The methods employed for studying decentralised data fusion pro...

Cooperative Data Fusion and Control Amongst Multiple Uninhabited Air Vehicles

This paper will present the latest research endeavors into decentralised data fusion and control when using multiple uninhabited air vehicles. The paper will focus on the development of Decentralised Picture Compilation and Decentralised SLAM algorithms when applied to a 6DoF platform. The issues associated in the development of the algorithms and the conclusions drawn from such an endeavor wil...

Decentralised Data Fusion with Delayed States for Consistent Inference in Mobile Ad Hoc Networks

Delayed-state decentralised data fusion (DS-DDF) is proposed as a general methodology for consistent DDF, which does not impose constraints on network topology. The resulting estimates, although lagged in time, are optimal, equal to a centralised solution. The method is demonstrated in the context of dynamic node tracking and localisation, where a team of mobile robots track each other’s positi...

A Beginners Guide to Decentralised Data Fusion

Multi-level State Estimation in an Outdoor Decentralised Sensor Network

Decentralised estimation of heterogeneous sensors is performed on an outdoor network. Attributes such as position, appearance, and identity represented by non-Gaussian distributions are used in in the fusion process. It is shown here that real-time decentralised data fusion of non-Gaussian estimates can be used to build rich environmental maps. Human operators are also used as additional sensor...