Asynchronous track fusion with global feedback

Abstract

Asynchronous fusion is one of the barriers among multisensor data fusion, and asynchronous track fusion is an important research aspect of asynchronous fusion for its practicability in application. At present, local prediction weighted fusion method is used extensively in processing asynchronous track fusion. In the current algorithms, the global fusion estimate isn't obtained by local sensors; thereby the local estimate can't be improved by the global estimate. This is because that there is no feedback communication link between the fusion center and local sensors, accordingly, the performance of the track fusion system is reduced. In order to improve the estimate precision of the fusion system, the global feedback is introduced in this paper, and the corresponding asynchronous track fusion algorithm is presented. Compared with the current algorithm without feedback, the proposed algorithm can effectively improve the estimate precision not only in local sensors but also in the fusion center, and the proofs are given in the appendix. The simulations and algorithm analysis both show the advantages of the novel algorithm.