Analysis of Adaptive Kronecker Sampled-Function Weighted Order Filters

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Published: Dec 29, 2017
Keywords:
Estimation nonlinear filters weighted order filter switching filter data-reusing approach adaptive algorithm

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Suchada Sitjongsataporn
Mahanakorn Institute of Innovation, Mahanakorn University of Technology
Piyaporn Nurarak
School of Science and Technology Sukhothai Thammathirat Open University

Abstract

This paper introduces a data-adaptive Kronecker filtering framework based on the data-reusing sampled-function weighted order (KSFWO) and switching KSFWO filters by means of data-reusing least mean square (DR-LMS) algorithm. The data-reusing algorithm is introduced and parameterized by the number of reuses of each weight update per data sample. We propose the adaptive KSFWO and switching KSFWO filters based on DR-LMS algorithm with the smoothing and robust characteristics. The coefficients of proposed filters are the samples of bounded real-valued function. These filters can be designed in form of a stochastic gradient filter. The proposed filters can be performed the robust smoothing filtering in some applications.

Article Details

Issue
Vol. 20 No. 2 (2017): July - December 2017
Section
Research Articles

Copyright @2021 Engineering Transactions 

Faculty of Engineering and Technology

Mahanakorn University of Technology

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