On-board real-time state and fault identification for rovers

For extended autonomous operation, rovers must identify potential faults to determine whether its execution needs to be halted or not. At the same time, rovers present particular challenges for state estimation techniques: they are subject to environmental influences that affect sensor readings during normal and anomalous operation, and the sensors fluctuate rapidly due to both the noise and the dynamics of rover´s interaction with its environment. This paper presents MAKSI, an on-board method for state estimation and fault diagnosis that is particularly appropriate for rovers. The method is based on a combination of continuous state estimation, using Kalman filters, and discrete state estimation using Markov-model representation