Test statistics for low complexity change detection in dynamic systems based on averaged filter models

• 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.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)

Nyckelord

Adaptive filtering

Algorithms

Convergence of numerical methods

Kalman filtering

Least squares approximations

Mathematical models

Parameter estimation

Signal detection

Statistical tests

Averaging theory

Normalized least mean square (NLMS)

Signal processing

Publikations- och innehållstyp

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