Rao-Blackwellised Point-Mass Smoothers for a Class of Conditionally Linear Dynamic Models
Jindřich Duník, Ondřej Straka · 2019
The paper deals with the state estimation of nonlinear stochastic dynamic systems. The stress is laid on the numerical solution to the Bayes' rule considering a class of conditionally linear Gaussian models typically appearing in navigation. In particular, three novel Rao-Blackwellised smoothers are proposed, where the nonlinear part of the model is solved by a computationally expensive point-mass smoother, whereas the conditionally linear part is solved by a set of linear smoothers. The proposed smoothers offer a tradeoff between the computational complexity and smoothing performance. The properties of the smoothers are theoretically analysed and discussed.