A General Method for Approximating Nonlinear Transformations of Probability Distributions

In this paper we describe a new approach for generalised nonlinear ltering. We show that the technique is more accurate, more stable, and far easier to implement than an extended Kalman lter. Several examples are provided, including the application of the new lter to problems involving discontinuous functions.