Description d'un detecteur sequentiel de changements brusques de dynamiques des modeles arma

  • D. Canon
  • C. Dongarli
Session 4 Detection Of Changes In Systems
Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 62)


The sequential detection of abrupt changes in ARMA models is an important question in multi sensor/multi target tracking or similar problems (failure detection, E.E.G. analysis ...)

The authors propose an original sequential algorithm both testing the model and estimating its parameters simultaneously. This method is based upon a constant order hypothesis with abrupt changes within the parameters of an ARMA model. This realistic hypothesis is a consequence of the experimental capability of a low order ARMA model to represent correctly any Markovian process.

The identification of the system is sequentially performed by an Extended Kalman Filter which provides both the parameters of the model and a "pseudo-innovation" sequence.

That independant sequence is multiply by itself with a link of one step to define a decision variable and to detect a change of level.


Extend Kalman Filter Nous Avons ARMA Model Realistic Hypothesis Premier Temp 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag 1984

Authors and Affiliations

  • D. Canon
    • 1
  • C. Dongarli
    • 1
  1. 1.Laboratoire D'AutomatiqueEcole Nationale Supérieure de MécaniqueNantes CédexFrance

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