Analytical performance evaluation for autonomous sensor fusion

Abstract

A distributed data fusion system consists of a network of sensors, each capable of local processing and fusion of sensor data. There has been a great deal of work in developing distributed fusion algorithms applicable to a network centric architecture. Currently there are at least a few approaches including naive fusion, cross-correlation fusion, information graph fusion, maximum a posteriori (MAP) fusion, channel filter fusion, and covariance intersection fusion. However, in general, in a distributed system such as the ad hoc sensor networks, the communication architecture is not fixed. Each node has knowledge of only its local connectivity but not the global network topology. In those cases, the distributed fusion algorithm based on information graph type of approach may not scale due to its requirements to carry long pedigree information for decorrelation. In this paper, we focus on scalable fusion algorithms and conduct analytical performance evaluation to compare their performance. The goal is to understand the performance of those algorithms under different operating conditions. Specifically, we evaluate the performance of channel filter fusion, Chernoff fusion, Shannon Fusion, and Battachayya fusion algorithms. We also compare their results to Naive fusion and optimal centralized fusion algorithms under a specific communication pattern.