Chapter 10. Change Detection Based on Multiple Models
Published Online: 16 OCT 2001
DOI: 10.1002/0470841613.ch10
Copyright © 2000 John Wiley & Sons, Ltd
Book Title
Adaptive Filtering and Change Detection
Additional Information
How to Cite
Gustafsson, F. (2001) Change Detection Based on Multiple Models, in Adaptive Filtering and Change Detection, John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/0470841613.ch10
Publication History
- Published Online: 16 OCT 2001
ISBN Information
Print ISBN: 9780471492870
Online ISBN: 9780470841617
- Summary
- Chapter
Keywords:
- data segmentation;
- change detection;
- jump Markov model;
- piecewise linear regression model;
- Matlab;
- Matlab toolbox;
- autotuning;
- signal processing;
- adaptive signal processing;
- adaptive systems;
- fault diagnosis;
- monitoring;
- surveillance;
- signal detection;
- decision making;
- statistics;
- stochastic processes;
- modeling;
- estimation;
- identification;
- parameter estimation;
- state estimation;
- observers;
- stochastic systems;
- time-varying systems;
- discrete time systems;
- sampled data systems;
- filters;
- communication systems;
- adaptive control
Summary
This chapter addresses the most general problem formulation of detection in linear systems. Basically, all problem formulations that have been discussed so far are included in the framework considered. A general jump linear state space model is used, which switches between a finite or infinite number of modes. The main purpose is to survey multiple model algorithms, and a secondary purpose is to overview and compare the state of the art in different application areas for reducing complexity, where similar algorithms have been developed independently.
Particular algorithms include the generalized pseudo Bayes (GPB), interacting multiple model (IMM), adaptive forgetting through multiple models (AFMM), Gibbs-Metropolis and Markov chain Monte Carlo (MCMC) algorithms.