A multiple model multiple hypothesis filter for Markovian switching systems

Authors:
Yvo Boers;Hans Driessen
Affiliations:
THALES Nederland B.V., Zuidelijke Havenweg 40, 7544 RR Hengelo, The Netherlands;THALES Nederland B.V., Zuidelijke Havenweg 40, 7544 RR Hengelo, The Netherlands
Venue:
Automatica (Journal of IFAC)
Year:
2005

Citing 3
Cited 3

Optimal Control of Stochastic Systems

Optimal Control of Stochastic Systems
Brief paper: Detection and estimation for abruptly changing systems

Automatica (Journal of IFAC)
Brief Hybrid state estimation: a target tracking application

Automatica (Journal of IFAC)

Brief paper: A convex optimization approach to filtering in jump linear systems with state dependent transitions

Automatica (Journal of IFAC)
A new hybrid state estimator for systems with limited mode changes

HSCC'07 Proceedings of the 10th international conference on Hybrid systems: computation and control
A novel truncated approximation based algorithm for state estimation of discrete-time Markov jump linear systems

Signal Processing

Quantified Score

Hi-index	22.15

Visualization

Abstract

In this paper, a new filtering method for hybrid Markovian switching systems is presented. The method is called the multiple model multiple hypothesis filter (M^3H filter). For each hypothesis an (extended) Kalman filter is running. An hypothesis represents a specific model mode sequence history. The proposed method is highly adaptive and flexible. The main feature is that the number of hypotheses that are maintained varies with the 'difficulty' of the situation and that it is adaptive in its computational load. In a representative example it is shown that the M^3H filter can outperform the widely used interacting multiple model (IMM) filter, both in terms of accuracy and computational load. The newly proposed filter is an excellent alternative for the widely used and celebrated IMM filter.