Active Noise Control Using Fast Kalman Filter Estimation.

A fast-array Kalman filter solution to active noise control

A Kalman filter solution to active control and its fast-array implementation are provided. The adaptive control problem is formulated as a state-estimation problem and no interchanging of the adaptive filter and the secondary-path is imposed. Moreover, no estimate of the disturbance signal is needed, and we exploit the structure in the state–space matrices to derive a fast-array implementation....

The Kalman Filter in Active Noise Control

Most Active Noise Control (ANC) systems use some form of the LMS [5][7] algorithm due to its reduced computational complexity. However, the problems associated with it are well-known, namely slow convergence and high sensitivity to the eigenvalue spread [3][7]. To overcome this problems the RLS algorithm is often used, but it is now widely known, that the RLS loses many of its good properties f...

Improved Kalman Filter Initialisation using Neurofuzzy Estimation

It is traditional to initialise Kalman filters and extended Kalman filters with estimates of the states calculated directly from the observed (raw) noisy inputs7, 9, 10 but unfortunately their performance is extremely sensitive to state initialisation accuracy (Figure 1). Good initial state estimates ensure fast convergence whereas poor estimates may give rise to slow convergence or even filter...

Kalman filter based modeling and constrained H∞ optimal control for active noise cancellation

Active Noise Control in Pardis Coach using Different Fuzzy Controllers

In recent years, need to increase the convenience of trips in railway vehicles causes that train operators and manufacturers focus on reducing the noise level which is sensed by passengers. In this paper, first the state of modeling acoustic noise in cab train is discussed and natural frequencies and acoustic mode shapes are derived and then formulation of acoustic pressure in the cab will be o...