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Completed TRL 2 (started at 2, targeting 3)
A novel approach to the estimation of dynamic systems operating in high-uncertainty environments is proposed. The proposed framework reconstructs the dynamics and measurement modeling found in modern spacecraft navigation filters using measure and possibility-theoretic approaches. Possibilistic filtering represents a new paradigm in parametric estimation techniques. Traditional methods in filtering rely on random process and tuning noise parameters to artificially inflate the uncertainty of a system. This approach is not derived from the physical nature of the system and risks the introduction of subjectivity into filtering solutions. The proposal then reframes uncertainty in dynamic systems as originating from two distinct sources: aleatory, or random, interactions(similar to modern filtering approaches) and epistemic uncertainty, introduced from a lack of knowledge about the system as a whole. Using the mathematical properties of outer probability measures and possibility functions, autonomous navigation in previously unexplored and sparsely characterized environments is facilitated. The development of a novel possibilistic navigation filter algorithm is proposed based on a reformulation of modern navigation techniques. The proposal specifically targets applications of possibilistic filtering to modern navigation challenges, such as terrain relative navigation, multitarget tracking, and robust, fault-tolerant, autonomous vehicle navigation.
The goal of this project is to facilitate autonomous navigation in previously unexplored and sparsely characterized environments, specifically targeting applications of possibilistic filtering to modern navigation challenges, such as terrain relative navigation, multitarget tracking, and robust, fault-tolerant, autonomous vehicle navigation.
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