A new solution to parameter adaptive estimation of random processes

Abstract

This paper is concerned with the development of an adaptive state estimator that is capable of tracking switched linear plants that undergo rapid configuration changes. The particular adaptive estimator developed here is called the Sliding Window Detector/Estimator (SWDE) algorithm. Unlike previous algorithms, the SWDE algorithm is designed specifically for the switched-linear plant problem. It uses a joint detection/estimation approach to give a very close approximation to the unrealizable optimum switched-linear estimator. An extremely reliable and accurate estimator can be constructed by combining a modified Parameter Adaptive Estimation (PAE) algorithm with SWDE. The algorithm has been fully verified by extensive computer simulation, and the implementation advantages afforded by this method make it suitable for use in a wide variety of applications.