Nonlinear estimation framework: A versatile tool for state estimation

The paper presents MATLAB based software framework designed for nonlinear state estimation of discrete-time dynamic systems. The framework is designed to facilitate implementation, testing and use of various nonlinear state estimation methods. It allows simple description of the problem, specification of estimation experiment and processing the resulting data in order to simply compare various estimators. The main strength of the framework is its versatility. The framework provides means for description of the problem by either structural or probabilistic model. The user has a wide variety of classic and contemporary estimation methods at hand, which can be easily parametrized. Besides the well-known basic nonlinear estimation methods such as the extended Kalman filter, the divided difference filters and the unscented Kalman filter, the framework implements the particle filter with advanced features. The framework offers tools for straightforward evaluation of many well known metrics used for estimate quality assessment. And as the framework is designed on the object oriented basis, further extension by user-specified nonlinear estimation algorithms is extremely easy. The aim of the paper is to get acquainted with the possibilities of the toolbox. It will demonstrate the easy and natural way in which the estimation problem can be described within the means provided by the framework. Two examples of target tracking will be demonstrated with the estimation experiment setup using the presented framework.