Efficient delay-tolerant particle filtering through selective processing of out-of-sequence measurements

This paper proposes a novel algorithm for delay-tolerant particle filtering that is computationally efficient and has limited memory requirements. The algorithm estimates the informativeness of delayed (out-of-sequence) measurements (OOSMs) and immediately discards uninformative measurements. More informative measurements are then processed using the storage efficient particle filter proposed by Orguner et al. If the measurement induces a dramatic change in the current filtering distribution, the particle filter is re-run to increase the accuracy. Simulation experiments provide an example tracking scenario where the proposed algorithm processes only 30-40% of all OOSMs using the storage efficient particle filter and 1-3% of OOSMs by re-running the particle filter. By doing so, it requires less computational resources but achieves greater accuracy than the storage efficient particle filter.