A 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