Grid-Based Bayesian Filters With Functional Decomposition of Transient Density

Abstract

The paper deals with the state estimation of nonlinear stochastic dynamic systems with special attention to grid-based Bayesian filters such as the point-mass filter (PMF) and the marginal particle filter (mPF). In the paper, a novel functional decomposition of the transient density describing the system dynamics is proposed. The decomposition approximates the transient density in a closed region. It is based on a non-negative matrix/tensor factorization and separates the density into functions of the future and current states. Such decomposition facilitates a thrifty calculation of the convolution involving the density, which is a performance bottleneck of the standard PMF/mPF implementations. The estimate quality and computational costs can be efficiently controlled by choosing an appropriate decomposition rank. The performance of the PMF with the transient density decomposition is illustrated in a terrain-aided navigation scenario and a problem involving a univariate non-stationary growth model.