Optimal flight path update with adding or removing out-of-sequence measurements

In a multisensor target tracking system, observations produced by sensors can arrive at a central processor out of sequence. There have been some state estimate update algorithms for out-of-sequence measurements (OOSMs). In this paper, we propose a flight path update algorithm for a sequence with arbitrary delayed OOSMs. The new algorithm has three advantages: 1) it is a globally optimal recursive algorithm; 2) it is an algorithm for arbitrary delayed OOSMs including the case of interlaced OOSMs with less storages, compared with the optimal state update algorithm in; 3) it can update the current whole flight path other than only the current single state with less computation, i.e., the dimension of the matrices which need to be inverted is not more than that of the state in process of updating the past ℓ (lag steps) estimates and corresponding error covariances. Besides, this algorithm can be easily modified to derive a globally optimal flight path update with removing an earlier (incorrectly associated) measurement.