Performance analysis of structured pedigree distributed fusion systems

Abstract

Structured pedigree is a way to compress pedigree information. When applied to distributed fusion systems, the approach avoids the well known problem of information double counting resulting from ignoring the cross-correlation among fused estimates. Other schemes that attempt to compute optimal fused estimates require the transmission of full pedigree information or raw data. This usually can not be implemented in practical systems because of the enormous requirements in communications bandwidth. The Structured Pedigree approach achieves data compression by maintaining multiple covariance matrices, one for each uncorrelated source in the network. These covariance matrices are transmitted by each node along with the state estimate. This represents a significant compression when compared to full pedigree schemes. The transmission of these covariance matrices (or a subset of these covariance matrices) allows for an efficient fusion of the estimates, while avoiding information double counting and guaranteeing consistency on the estimates. This is achieved by exploiting the additional partial knowledge on the correlation of the estimates. The approach uses a generalized version of the Split Covariance Intersection algorithm that applies to multiple estimates and multiple uncorrelated sources. In this paper we study the performance of the proposed distributed fusion system by analyzing a simple but instructive example.