Distributed Multisensor Estimation Fusion with Out-of-Sequence Measurements

Abstract

In the multiple sensor/sub-processor system, distributed estimation fusion based on the two level optimization strategy (optimal sensor estimations and optimal processor center fusion) are used widely. Optimal distributed estimation fusion with out-of-sequence measurements (OOSM) at local sensors is presented in this paper, its performance is equivalent to that of the corresponding Kalman filtering using all sensor observations (which is called the centralized Kalman filtering fusion).