Analysis of Adaptive Kronecker Sampled-Function Weighted Order Filters

Main Article Content

Suchada Sitjongsataporn
Piyaporn Nurarak


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

Research Articles


R. Oten and R.J.P. de Figueiredo, “Sampled-Function Weighted Order Filters”, IEEE Transaction on Circuits and Systems II: Analog and Digital Signal Processing, vol. 49, no. 1, pp. 1-10, Jan. 2002.

J. Astola and P. Kuosmanen, “Fundamentals of Nonlinear Digital Filtering”, CRC, 1997.

R. Oten and R.J.P. de Figueiredo, “Adaptive SFWO filter design”, in Proc.IEEE International Conference Image Processing (ICIP), vol. 2, pp. 982-984, Oct. 1998.

F. Palmieri and C.G. Boncelet, Jr., “Ll-Filters – A New Class of Order Statistics Filters”, IEEE Transaction on Acoustics, Speech and Signal Processing, vol. 37, no. 5, May 1989.

M.Meguro and Y. Kawashima, “A New Robust Low-pass and High-pass Filtering Method by using Ll filters”, in Proc. International Workshop on Smart Info-Media Sys. In Asia (SISA), pp.49 - 54, Sep. 2012.

D.P. Mandic and V.S.L. Goh, “Complex Valued Nonlinear Adaptive Filters: Noncircularity, Widely Linear and Neural Models”, John Wiley& Sons, 2009.

B.A. Schnaufer and W.K. Jenkins, “New Data-Reusing LMS Algorithms for Improved Convergence”, in Proc. 27th Asilomar Conference on Signals and Systems, vol. 2, pp. 1584 - 1588, 1993.

S. Roy and J.J. Shynk, “Analysis of the Data-Reusing LMS Algorithm”, in Proc. IEEE Midwest Symposium on Circuits and Systems (MWCAS), pp. 1127-1130, 1989.

S.Sitjongsataporn and M.Kasuga, “Adaptive Data-Reusing Kronecker Sampled-Function Weighted Order Filters”, International Workshop on Advanced Image Technology (IWAIT), Nagoya, Japan, pp. 1113-1117, Jan. 2013.

S.Sitjongsataporn and P.Nurarak, “Adaptive Frequency-Domain Switching Kronecker Sampled-Function Weighted Order Filters”, International Technical Conference on Circuits/Systems, Computers and Communications(ITC-CSCC), Yeosu, Korea, pp. 613-616, July 2013.

V. Katkovnik, “Multiresolution Local Polynomial regression: A New Approach to Pointwise spatial adaptation”, in Proc. Digital Signal Processing, vol.15, pp. 73-116, 2005.

P.S.R Diniz, “Adaptive Filtering Algorithms and Proactical Implementation”, Springer, 2008.