Paper Info
Title
Adaptive Normal-Form State-Space Notch Filters.
Abstract
This paper explores novel adaptive state-space notch filters with novel update laws by applying the normal-form state-space realization of a single frequency second-order IIR notch filter. An adaptive iterative algorithm is obtained by employing a gradient descent method for minimizing the mean-squared output error of an adaptive notch filter. An alternative adaptive iterative algorithm is found to be a simplified form of the above technique. Moreover, the step-size bound characterized by the gradient of the mean update term is deduced in each case. It is noted that the normal-form state-space realization has minimum pole sensitivity. A numerical example is presented to demonstrate the validity and effectiveness of the proposed adaptive state-space notch filters.
Year
DOI
Venue
2018
10.1109/ICDSP.2018.8631647
DSL
Keywords
Field
DocType
Iterative methods,Speech processing,Convergence,Frequency estimation,State-space methods,Transfer functions,Notch filters
Convergence (routing),Computer vision,Band-stop filter,Speech processing,Gradient descent,Computer science,Iterative method,Infinite impulse response,Algorithm,Transfer function,Artificial intelligence,State space
Conference
ISSN
ISBN
Citations 
1546-1874
978-1-5386-6811-5
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Y. Hinamoto1679.93
Shotaro Nishimura2208.91