Chapter 9. Change Detection Based on Likelihood Ratios

  1. Fredrik Gustafsson

Published Online: 16 OCT 2001

DOI: 10.1002/0470841613.ch9

Adaptive Filtering and Change Detection

Adaptive Filtering and Change Detection

How to Cite

Gustafsson, F. (2001) Change Detection Based on Likelihood Ratios, in Adaptive Filtering and Change Detection, John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/0470841613.ch9

Author Information

  1. Linkoping University, Linkoping, Sweden

Publication History

  1. Published Online: 16 OCT 2001

ISBN Information

Print ISBN: 9780471492870

Online ISBN: 9780470841617

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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 is devoted to the problem of detecting additive abrupt changes in linear state space models. Sensor and actuator faults as a sudden offset or drift can all be modeled as additive changes. In addition, disturbances are traditionally modeled as additive state changes. The likelihood ratio formulation provides a general framework for detecting such changes, and to isolate the fault/disturbance. The main algorithms are the generalized likelihood ratio (GLR) test and the marginalized likelihood ratio test.