State estimation and multi-sensor data fusion using data-based neurofuzzy local linearisation process models

Abstract

Modelling unknown non-linear dynamic processes is an essential prerequisite for model-based state estimation and fusion. Fuzzy local linearisation (FLL) is a useful divide-and-conquer method for coping with complex problems such as data-based non-linear process modelling. In this paper, a hybrid learning scheme which combines a modified adaptive spline modelling (MASMOD) algorithm and the expectation-maximisation (EM) algorithm is developed for FLL modelling, based on which Kalman filter type algorithms for state estimation and multi-sensor data fusion are investigated. Two commonly used measurement fusion methods are analytically compared. A hierarchical multi-sensor data fusion architecture is proposed, with an example of non-linear trajectory estimation to validate the proposed method, which integrates the techniques for FLL modelling, neurofuzzy state estimation and multi-sensor data fusion. Whilst this paper mainly focuses on state estimation and data fusion for unknown non-linear dynamic processes, maneuvering targets are also briefly considered.