Adaptive Filtering with Reinforcement Learning against Model Mismatch in Low-Observable Settings

State estimation of dynamical systems is a central problem in many applications. Optimal solutions for such problems are well established when the system dynamics are completely known (i.e., Kalman filter in the linear–Gaussian setting). When the assumed models are mismatched from the true system's transition, subspace identification methods are commonly employed but only under the assumption of fully observable systems.