Decentralised Data Fusion with Parzen Density Estimates

Abstract

Decentralised sensor networks typically consist of multiple processing nodes supporting one or more sensors. These nodes are interconnected via wireless communication. Practical applications of decentralised data fusion have generally been restricted to using Gaussian based approaches such as the Kalman or information filter. This paper proposes the use of Parzen window estimates as an alternate representation to perform decentralised data fusion. It is required that the common information between two nodes be removed from any received estimates before local data fusion may occur. Otherwise, estimates may become overconfident due to data incest. A closed form approximation to the division of two estimates is described to enable conservative assimilation of incoming information to a node in a decentralised data fusion network. A simple example of tracking a moving particle with Parzen density estimates is shown to demonstrate how this algorithm allows conservative assimilation of network information.