Out-of-sequence-measurement processing for probabilistic multiple hypothesis tracker with measurement reordering

In a multi-sensor central level tracking system, owing to random delay in transmission and varying preprocessing time for different sensor platforms, an earlier measurement from the same target can arrive at the fusion center after a later one. Practical data fusion schemes are challenged by the inevitable appearance of measurements that are out of sequence, called, “out-of-sequence measurements” (OOSMs). The question is how to incorporate these OOSMs in a track that has already been updated with a later observation in order to enhance the performance of the tracking system. Several approaches for a sequential algorithm to find a solution for the OOSM problem have been discussed in previous papers. An approach to address the OOSM problem in the probabilistic multi-hypothesis tracker (PMHT), being a batch algorithm, was proposed in previous paper. However, the situation of this approach was not an OOSM case but, rather, an out of sequence scan (OOSS) where a batch of data was lost and then only one scan of measurements from the lost batch arrived with the present batch. In this paper, we propose an approach that has a measurement reordering step to address the OOSM problem in the PMHT within the framework of the OOSM case and report on the performance with the simulation results. The simulation results indicate that the proposed approach may be a suitable solution for the OOSM problem in PMHT under the proper conditions of length of batch, amount of lag, density of clutter, and probability of detection for the target.