Globally Optimal Distributed Kalman Fusion With Local Out-of-Sequence-Measurement Updates

In a distributed multisensor fusion systems, observations produced by sensors can arrive at local processors out of sequence. The resulting problem at the central processor/fusion center-how to update current estimate using multiple local out-of-sequence-measurement (OOSM) updates - is a nonstandard distributed estimation problem. In this note, based on the centralized update algorithm with multiple asynchronous (1-step-lag) OOSMs see we firstly deduce the optimal distributed fusion update algorithm with multiple local asynchronous (1-step-lag) OOSM updates, which is proved, under some regularity conditions, to be equivalent to the corresponding optimal centralized update algorithm with all-sensor 1-step-lag OOSMs. Then, we propose an optimal distributed fusion update algorithm with multiple local arbitrary-step-lag OOSM updates.