Multiple-Model Adaptive Estimation for Measurements with Unknown Time Delay

If a sensor requires finite processing and communication time, then the current sensor reading actually corresponds to the states of a vehicle at some point in the past. At the sensor fusion algorithm, it is expected that large errors may accumulate over time if we simply ignore the fact that the sensor measurement is lagged. Over several decades, the Kalman filter based compensation techniques for the sensor time-delay were introduced. However, when the existence of measurement delays is not known or the delay values are uncertain, a multiple-model adaptive estimator converges to the correct model between a modified extended Kalman filter to handle the lagged measurements and a standard extended Kalman filter without time-delay compensation. Furthermore, the multiple Kalman filters running in parallel each generate an estimate of the state, and combine them to obtain a refined state estimate. The simulation work, including Monte Carlo results, is reported for a variety of cases.