Decision-Theoretic Monte Carlo Smoothing for Scaling Probabilistic Tracking in Hybrid Dynamic Systems
Vandi Verma, Reid G. Simmons · 2004
AbstructDetecting faults on-board planetary rovers is important since human intervention may not he possible due to communication delays. In this paper we propose a scalable method for on-board fault detection and identification that may be applied to general fault models with limited computation. Although our application focus is on diagnosing rover faults, this method is applicable in general for tracking any general non-linear, non-Gaussian hybrid (discretecontinuous) dynamic system online.