A New Paradigm for State Estimation in Nonlinear Systems via Minimum Distortion Filtering

It is well know that, for linear gaussian processes, the Kalman Filter provides an elegant solution to the recursive state estimation problem. However, when the process is nonlinear, then one needs to use various approximations to the filtering problem. In this paper we describe a new approach to nonlinear filtering based on Minimum Distortion Filtering. At the core of this algorithm is a deterministic on-line griding technique based on Vector Quantization. We explore several practical issues associated with the algorithm which are necessary to ensure that it runs effectively. We also present several examples which illustrate the utility of this class of algorithm in the context of nonlinear state estimation.