Suchada Sitjongsataporn
Mahanakorn Institute of Innovation (MII), Department of Electronic Engineering
Faculty of Engineering Mahanakorn University of Technology
140 Cheumsamphan Road, Bangkok10530 Thailand
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Piyaporn Nurarak
School of Science and Technology Sukhothai Thammathirat Open University
9/9 Chaengwattana Rd., Nonthaburi, Thailand
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Manuscript received November 27, 2016
Revised December 2, 2017


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.

Keywords: Estimation, nonlinear filters, weighted order filter, switching filter, data-reusing approach, adaptive algorithm.

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