Delayed-state sigma point Kalman filters for underwater navigation

Measurement delays are inherent in position feedback methods used for underwater navigation. Even for small delays, proper treatment of these measurements will provide more robust 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.