A new filter for hybrid systems and its applications to robust attitude estimation

Fault diagnosis and recovery are essential tools for the development of autonomous agents that can operate in hazardous environments. This can be effectively approached from a model-based perspective, where sensor faults are explicitly taken into account in a hybrid model with switching dynamics. However, practical hybrid filters are required to manage an exponential growth in the number of discrete mode sequences, also known as hypotheses. Inspired by an attitude estimation application for a quadrotor UAV with faulty sensors, this paper introduces the IP-MHMF, a novel filter for hybrid systems that generalizes the well-known IMM and introduces a more informed hypothesis-pruning step than previous algorithms. By performing hypothesis pruning on corrected rather than predicted hypothesis probabilities, the IP-MHMF is capable of much more aggressive pruning strategies that significantly reduce its computational load, while improving its estimation performance. Our numerical results on data from a real robotic platform show that the IP-MHMF outperforms state-of-the-art hybrid filters and the traditional EKF on an attitude estimation application with faulty magnetometer measurements.