Modeling stochastic systems in Riemannian manifolds

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This project aims to develop new statistical signal processing and control engineering algorithms and tools that will enable tracking of objects remotely on land and in space. A growing number of applications require the study of random processes in Riemannian manifolds, that is processes that evolve subject to a geometric constraint. This project aims to provide methods for the numerical simulation of such processes, methods of online and offline system identification from data on such processes and asymptotic performance analysis; and algorithms for process state estimation that obeys the geometry. The outcomes will advance and benefit spatio-temporal data analysis in areas such as transport, health and video-security.