Wenhan Li

• Regime-switching differentiable particle filters

In real-world applications, both dynamic and measurement models in state-spaces models (SSMs) can be constituted by a variety of modes and be switchable. For instance, in object tracking, targets can exhibit diverse status, e.g., people may stroll gently, ride in heavy traffic, or cruise on motorways at different times. On the other hand, the observational measurement can be susceptible to various conditions such as obstacle sheltering or weather evolving. Therefore, it leads to an additional complexity layer of the regime mixture uncertainty in SSMs, i.e., regime-switching state-space models. This project leads to analyse such complicated dynamical systems with satisfied flexibility and scalability through a trustworthy data-adaptive method instead of a heuristic way.