title: A mixture-of-experts framework for adaptive Kalman filtering.
abstract: This paper proposes a modular and flexible approach to adaptive Kalman filtering using the framework of a mixture-of-experts regulated by a gating network. Each expert is a Kalman filter modeled with a different realization of the unknown system parameters such as process and measurement noise. The gating network performs on-line adaptation of the weights given to individual filter estimates based on performance. This scheme compares very favorably with the classical Magill filter bank, which is based on a Bayesian technique, in terms of: estimation accuracy; quicker response to changing environments; and numerical stability and computational demands. The proposed filter bank is further enhanced by periodically using a search algorithm in a feedback loop. Two search algorithms are considered. The first algorithm uses a recursive quadratic programming approach which extremizes a modified maximum likelihood function to update the parameters of the best performing filter in the bank. This particular approach to parameter adaptation allows a real-time implementation. The second algorithm uses a genetic algorithm to search for the parameter vector and is suited for post-processed data type applications. The workings and power of the overall filter bank and the suggested adaptation schemes are illustrated by a number of examples.
authors.affiliation: Dept. of Aerosp. Eng. & Eng. Mech., Texas Univ., Austin, TX.
authors.forename: W S
authors.initials: WS
authors.lastname: Chaer
authors.forename: R H
authors.initials: RH
authors.lastname: Bishop
authors.forename: J
authors.initials: J
authors.lastname: Ghosh