Density Approximation Error Assessment and Compensation in Point-Mass Filter

This paper deals with the state estimation of non-linear stochastic dynamic systems with an emphasis on a probability density function approximation used by point-mass filters. Approximation error of the standard point-mass density is analysed and quantified, and a novel point-mass density approximation with inherent approximation error minimisation is developed. The properties of the proposed point-mass are theoretically analysed and numerically illustrated