THE NUMBER OF POLYNOMIAL SEGMENTS AND THE POLYNOMIAL ORDER OF POLYNOMIAL-BASED FILTERS
Keywords:
Decimation, Estimation formula, Farrow structure, Interpolation, Polynomial-based interpolation filtersAbstract
Many digital signal processing applications can benefit from polynomial-based interpolation filters based on the Farrow structure or its variations. The number of polynomial segments determining the finite length of the filter impulse response and the order of polynomials in each polynomial segment are the two main design parameters for these filters. These parameters are linked to the complexity of the implementation structure and frequency domain performance. As a result, determining the value of these two parameters based on system requirements is beneficial in order to estimate complexity of the filter, and starting values for a design. This paper offers formulas for estimating the length and polynomial order of polynomial-based filters for a variety of criteria, including stopband attenuation, transition bandwidth, passband deviation, and passband/stopband weighting.
References
(1) S. Curila, M. Curila, D. Curila, C. Grava, A mathematical model and an experimental setap for the rendering of the sky scene in a foggy day, Rev. Roum. Sci. Techn.– Électrotechn. et Énerg., 65, 3–4, pp. 265–270 (2020).
(2) J. Vesma, T. Saramäki, Interpolation filters with arbitrary frequency response for all-digital receivers, IEEE International Symposium on Circuits and Systems ISCAS 96, Atlanta, GA, USA, 1996.
(3) J. Vesma, T. Saramäki, Polynomial-based interpolation Filters - Part I: Filter synthesis, Circuits, Systems, and Signal Processing, 26, 2, pp. 115–146 (2007).
(4) C. W. Farrow, A continuously variable digital delay element, Proc. 1988 IEEE Int. Symp. Circuits and Systems, Espoo, pp. 2641, Finland, June 1988.
(5) D. Babic, T. Saramäki, M. Renfors, Conversion between arbitrary sampling rates using polynomial-based interpolation filters, in Proc. 2nd Int. TICSP Workshop on Spectral Methods and Multirate Signal 64.Processing SMMSP’02, pp. 57, Toulouse, France, September 2002.
(6) D. Babic, J. Vesma, T. Saramäki, M. Renfors, Implementation of the transposed Farrow structure, Proc. IEEE Int. Symp. Circuits and Systems, Scotsdale, 4, pp. 4–8 , Arizona, USA, 2002.
(7) H. Johansson, P. Löwenborg, On the design of adjustable fractional delay FIR filters, IEEE Trans. Circuits Syst. II, 50, 4, pp. 164–169, (2003).
(8) M. Unser, A. Aldroubi, and M. Eden, Fast B-spline transforms for continuous image representation and interpolation, IEEE Trans. Pattern Analysis and Machine Intelligence, 13, pp. 277–285 (1991).
(9) A. Gotchev, J. Vesma, T. Saramäki, K. Egiazarian, Multi-scale image representations based on modified B-splines, Proc. Int. Workshop on Spectral Techniques and Logic Design for Future Digital Systems, pp. 431– 452, Tampere, Finland, June 2000.
(10) M. Unser, A. Aldroubi, M. Eden, Polynomial spline signal approximations: Filter design and asymptotic equivalence with Shannon’s sampling theorem, IEEE Trans. Information Theory, 38, pp. 95-103, Jan. (1992).
(11) R J. Selva, An Efficient Structure for the Design of Variable Fractional Delay Filters Based on the Windowing Method, IEEE Trans. Signal Processing, 56, pp. 3770-3775, Aug. (2008).
(12) D. Babic, Windowing Design Method for Polynomial-Based Interpolation Filters, Circuits, Systems and Signal Processing, DOI: 10.1007/s00034-012-9486-y, (2012).
(13) D. Babic and H. G. Göckler. Estimation of the Length and the Polynomial Order of Polynomial-based Filters, The 8th International Conference on Sampling Theory and Applications (SAMPTA'09), Marseille, 18–22 May 2009.
(14) S. Vukotic, D. Babic, Estimation of Length and Order of Polynomial-based Filter Implemented in the Form of Farrow Structure, Engineering, Technology & Applied Science Research, 6, 4, pp. 1099–1102 (2016).
(15) T. Saramäki, Finite impulse response filter design, Chapter 4 in Handbook for Digital Signal Processing, edited by S. K. Mitra and J. F. Kaiser, John Wiley & Sons, New York, 1993.
(16) D. Babic, V. Lehtineni, M. Renfors, Discrete-time modeling of polynomial-based interpolation filters in rational sampling rate conversion, International Symposium on Circuits and Systems, ISCAS 2003, 4, pp. 321–324, Bang-kok, Thailand, May, 2003.
(17) T. Saramäki, Multirate Signal Processing, Lecture Notes, http://www.cs.tut.fi/~ts/.
Downloads
Published
Issue
Section
License
Copyright (c) 2021 Romanian Journal of Technical Sciences, Electrical and Energy Series
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.