Fusion Techniques Using Distributed Kalman Filtering for Detecting Changes in Systems

The objective of this paper is to compare the performance of two detecion strategies that are based on different data fusion techniques. The application of the detection strategies is to detect changes in a linear system. One detection strategy involves combining the estimates and eror covariance matrices of distributed Kalman filters, generating a residual from the fused estimates, comparing this residual to a threshold, and making a decision. The other detection strategy involves a distributed decision process in which estimates from distributed Kalman filters are used to generate distributed residuals which are compared locally to a threshold Local decisions are made and these decisions are then fused into a global decision. The relative performance of each of these detection schemes is compared and it is concluded that better performance is achieved when local decisions are made and then fused into a global decision.