Multi-sensor multi-target tracking with out-of-sequence measurements
Keshu Zhang, X.R. Li, Huimin Chen · 2003
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 ran- dom clutter in the possible OOSMs and thus the filter has to handle the measurement origin uncertainty. In this paper, we incorporate the probabilistic data asso- ciation (PDA) into the two OOSM update algorithms ALG-I and ALG-11 proposed previously. We present the algorithms ALG-I and ALG-11 in new forms with economic storage and efficient computation based on the nonsingularity assumption of some special matri- ces. Simulation results show that PDA with the two OOSM update algorithms have compatible RMS errors to the in-sequence PDA filter.