Multi-sensor multi-target tracking with out-of-sequence measurements

In multi-sensor target tracking systems, measurements from the same target can arrive out of sequence, called the out-of-sequence measurements (OOSMs). The resulting problem - how to update the current state estimates with the "old" measurements has been solved optimally and sub-optimally for one-lag as well as multi-lag OOSM update. In general, the existing algorithms assume perfect target detection and no clutter in the received measurements. The real world has, however, possible missed target detection and random clutter in the possible OOSMs and thus the filter has to handle the measurement origin uncertainty. In this paper, we incorporate the probabilistic data association (PDA) into the two OOSM update algorithms ALG-I and ALG-II proposed previously. We present the algorithms ALG-I and ALG-II in new forms with economic storage and efficient computation based on the nonsingularity assumption of some special matrices. Simulation results show that PDA with the two OOSM update algorithms have compatible RMS errors to the in-sequence PDA filter.