Delayed-state sigma point Kalman filters for underwater navigation


Measurement delays are inheient in position feedback methods used fot undeiwatei navigation. Even foi small delays, ptopei treatment of these measurements will provide mote lobnst performance and reduce uncertainties, improving overall precision. The Kalman filter (KF) can be adapted to treat delayed measurements in an efficient and mathematically rigorous way. We present a delayed state sigma point Kalman filter (SPKF) implementation for underwater navigation using delayed position measurements. The implementation includes a novel model-based approach to fusing the delayed measurements, with the ability to handle varying delays. We provide an example mission scenario where a surface tender with an ultra-short baseline (USBL) system tracks a submerged vehicle. We use this example to renavigate field data from recent deployments of the National Deep Submergence Facility (NDSF) autonomous underwater vehicle (AUV) Sentry, and compare estimates from a delay-compensated filter to those from a filter that ignores the delay.

IEEE/OES Autonomous Underwater Vehicles