Neural Augmented Adaptive Grid Design for Point-Mass Filter

Abstract

This paper deals with the state estimation of nonlinear systems described by dynamic stochastic state-space models using a point-mass filter (PMF). The PMF is based on the approximation of the conditional probability density function by a piece-wise constant probability density, called the point-mass density (PMD), where the probability is evaluated at N grid points. The number of grid points significantly affects both the performance and computational complexity of the PMF. However, N is typically regarded as a user-defined parameter. The aim of this paper is to augment the PMF with a neural network (NN). This NN selects the smallest N that leads to the required estimation accuracy thus ensuring the minimal computational complexity.