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A test statistics for low complexity change detection in dynamic systems based on averaged filter models
KTH, Superseded Departments, Signals, Sensors and Systems.ORCID iD: 0000-0002-1927-1690
1996 (English)In: Acoustics, Speech, and Signal Processing, 1996. ICASSP-96. Conference Proceedings., 1996 IEEE International Conference on, 1996, Vol. 5, 2845-2848 p.Conference paper (Refereed)
Abstract [en]

This paper presents a new method to detect and discriminate between abrupt changes in dynamics and sudden changes in disturbance levels in dynamic systems. It is assumed that a normalized least mean square (NLMS) adaptive filter estimates the system. The detection method is based on the observation that the estimated taps behave differently in the two studied events. The convergence behavior of the taps is modeled using averaging theory, giving an exponential convergence behavior for each tap. Kalman filtering techniques, based on this model, are then used in order to design a new detection scheme

Place, publisher, year, edition, pages
1996. Vol. 5, 2845-2848 p.
Keyword [en]
Kalman filtering techniques;NLMS adaptive filter;abrupt change detection;abrupt change discrimination;averaged filter models;averaging theory;detection method;disturbance levels;dynamic systems;estimated taps;exponential convergence behavior;low complexity change detection;normalized least mean square;sudden changes;test statistics;adaptive Kalman filters;adaptive signal processing;computational complexity;convergence of numerical methods;filtering theory;least mean squares methods;parameter estimation;signal detection;statistical analysis;
National Category
Control Engineering
URN: urn:nbn:se:kth:diva-57954DOI: 10.1109/ICASSP.1996.550146OAI: diva2:472754
NR 20140805Available from: 2012-01-04 Created: 2012-01-04 Last updated: 2013-09-05Bibliographically approved

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Wahlberg, Bo
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