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The transmission modes of multi-hop and broadcasting for Wireless Sensor Networks(WSN)often make random and unknown transmission delays appear,so multisensor data fusion based ondelayed systems attracts intense attention from lots of researchers.The existing achievements for thedelayed fusion all focus on Out-Of-Sequence Measurements(OOSM)problem which has many dis-advantages such as high communication cost,low computational efficiency,huge computational com-plexity and storage requirement,bad real-time performance and so on.In order to overcome theseproblems occurred in the OOSM fusion,the Out-Of-Sequence Estimates(OOSE)are considered tosolve the delayed fusion for the first time.Different from OOSM which belongs to the centralized fusion,the OOSE scheme transmits local estimates from local sensors to the central processor and is thus thedistributed fusion;thereby,the OOSE fusion can not only avoid the problems suffered in the OOSMfusion but also make the design of fusion algorithm highly simple and easy.Accordingly,a novel optimallinear recursive prediction weighted fusion method is proposed for one-step OOSE problem in this letter.As a tradeoff,its fusion accuracy is slightly lower than that of the OOSM method because the currentOOSM fusion is a smooth estimate and OOSE gets a prediction estimate.But,the smooth result of theOOSE problem also has good fusion accuracy.Performance analysis and computer simulation show thatthe total performance of the proposed one-step OOSE fusion algorithm is better than the current one-step OOSM fusion in the practical tracking systems.
The transmission modes of multi-hop and broadcasting for Wireless Sensor Networks (WSN) often make random and unknown transmission delays appear, so multisensor data fusion based on dependent systems attracts intense attention from lots of researchers. The existing achievements for the delay in fusion all focus on Out -Of-Sequence Measurements (OOSM) problem which has many dis-advantages such as high communication cost, low computational efficiency, huge computational com-plexity and storage requirement, bad real-time performance and so on.In order to overcome theseproblems occurred in the OOSM fusion, the Out-Of-Sequence Estimates (OOSE) are considered tosolve the delayed fusion for the first time. Different from OOSM which belongs to the centralized fusion, the OOSE scheme transmitted local estimates from local sensors to the central processor and is thus thedistributed fusion; thereby, the OOSE fusion can not only only avoid the problems suffered in the OOSMfusion but also make the design of fusion algorithm hi ghly simple and easy. According to a novel optimallinear recursive prediction weighted fusion method proposed for one-step OOSE problem in this letter. As a tradeoff, its fusion accuracy is slightly lower than that of the OOSM method because the current OOSM fusion is a smooth estimate and OOSE gets a prediction estimate. But, the smooth result of the OOSE problem also has good fusion accuracy. Performance analysis and computer simulation show that the total performance of the proposed one-step OOSE fusion algorithm is better than the current one-step OOSM fusion in the practical tracking systems.