Introduces principles and techniques for designing, implementing, and analyzing probabilistic state estimators for dynamical systems that require going beyond traditional least-squares and Kalman filtering approaches. Emphasis on development of practical discrete-time Bayesian state space filtering algorithms for systems characterized by partial observability and non-Gaussian uncertainties, which arise in many applications governed by complex non-linear stochastic dynamics and measurement processes.
