A Tractable Approach to Probabilistically Accurate Mode Estimation

As space exploration missions grow increasingly complex and are required to operate over extended durations of time, there is an amplified demand for accurate autonomous health management systems. In response, model-based programming approaches have been developed as scalable solutions to autonomous health management. Previous model-based monitoring and diagnosis techniques, such as Livingstone, were demonstrated to successfully and efficiently track nominal and failure modes in an abundance of scenarios, but at the cost of making simplifying assumptions about the observation probability that can lead to erroneous diagnoses after extended operation. Extending on Best-First-Belief-StateEnumeration (BFSE), this paper presents a new mode estimation technique called Best-First Belief State Update (BFBSU) that eliminates the observation probability assumption. BFBSU uses the full two-stage HMM belief state update equations as its utility function, thus further increasing estimator accuracy, while maintaining the efficiency required for real-time monitoring and fault detection.