SciELO - Scientific Electronic Library Online

vol.10 issue2EditorialChecking Untimed and Timed Linear Properties of the Interval Timed Colored Petri Net Model: Verificación de las propiedades lineales síncronas y asíncronas del Modelo de la Red de Petri Coloreado Intervalo Tiempo author indexsubject indexsearch form
Home Pagealphabetic serial listing  

Services on Demand




Related links

  • Have no similar articlesSimilars in SciELO


Computación y Sistemas

Print version ISSN 1405-5546

Comp. y Sist. vol.10 n.2 México Oct./Dec. 2006


An Algorithm for Computing Design Parameters of IFIR Filters with Low Complexity


Algoritmo para el Cálculo de Parámetros de Diseño de Filtros IFIR de Baja Complejidad


Javier Díaz–Carmona1, Gordana Jovanovic–Dolecek2 and José A. Padilla–Medina1.


1 Technological Institute of Celaya, Ave. Tecnologico y G. Cubas, Postal Code 38010, Celaya, Guanajuato, México. email: ;

2 National Institute of Astrophysics Optics and Electronics, P. O. Box 51 and 216, Postal Code 72000, Puebla, Puebla, México. email:


Article received on August 17, 2006
Accepted on November 11, 2006



This paper describes an algorithm for computing design parameters of Interpolated FIR (IFIR) digital filters with a low complexity (small number of products per output sample). The interpolation function is performed by a sharpened cascaded Recursive–Running Sum (RRS) filter, where the sharpening technique is used to improve the RRS frequency domain characteristics. Given the desired filter specifications and a chosen sharpening polynomial, the proposed algorithm computes the maximum IFIR filter interpolation factor and the minimum number of stages of the RRS filter in such a way that the overall structure meets the desired specifications with the minimum complexity.

Keywords: Narrowband filtering, Digital filters, IFIR, Sharpening technique, RRS filter.



En este artículo se describe un algoritmo para calcular los parámetros de diseño de filtros digitales FIR interpolados (IFIR) con una complejidad baja (número pequeño de productos por muestra de salida). La función de interpolación se lleva a cabo mediante un filtro de suma en línea recursivo (RRS) moldeado, en donde la técnica de moldeo se aplica para mejorar las características en el dominio de la frecuencia del filtro RRS. Dadas las especificaciones deseadas del filtro y un polinomio de moldeo, el algoritmo propuesto calcula el máximo factor de interpolación del filtro IFIR y además el número mínimo de etapas en cascada del filtro RRS de manera que la estructura completa satisfaga las especificaciones con la mínima complejidad.

Palabras Clave: Filtrado de banda angosta, Filtros digitales, IFIR, Técnica de moldeo, Filtro RRS.





1. Gustafsson, O., H. Johansson and L. Wanhammar, "Narrow–band and wide–band single filter frequency masking FIR filters." in IEEE International Symposium on Circuits and Systems, Sidney, Australia, 2001, pp. 181–184.        [ Links ]

2. Mehrnia A. and N. Willson Jr., "On optimal IFIR filter design." in IEEE International Symposum on Circuits and Systems, Columbia Canada, May, 2004, pp. 133–136.        [ Links ]

3. Mitra S., Digital Signal Processing: a Computer–based Approach, McGraw–Hill, New York, 2005.        [ Links ]

4. Mitra S., A. Mahalanobis and T. Saramaki, "A generalized structural subband decomposition of FIR filters and its application in efficient FIR filter design and implementation." IEEE Transactions on Circuits and Syst.–II: Analog and Digital Signal Processing, Vol. 40, 1993, pp. 363–374.        [ Links ]

5. Neuvo Y., D. Cheng–yu and S. Mitra , "Interpolated finite impulse response filters." IEEE Transaction on Acoustics Speech, and Signal Processing, Vol. ASSP–32, 1984, pp. 563–570.        [ Links ]

6. Hartnett R. J. and G. F. Boudreaux , "Improved filter sharpening polynomial." IEEE Transactions on Signal Processing, Vol. 43, 1995, pp. 2805–2810.        [ Links ]

7. Jovanovic–Dolecek, G. and J. Diaz–Carmona, "One structure for efficient narrowband bandpass FIR filters." in IEEE International. Midwest Symposium, Oklahoma, USA, August, 2002, pp. 485–488.        [ Links ]

8. Kaiser J. F. and R. W. Hamming, "Sharpening the response of a symmetric nonrecursive filter by multiple use of the same filter." IEEE Transactions on Acoustics Speech and Signal Processing, Vol ASSP–32, 1984, pp. 415–422.        [ Links ]

9. Kwentus A. Y., Z. Jiang and N. Willson Jr. , "Application of a filter sharpening to cascaded integration–comb decimator filters." IEEE Transactions on Signal Processing, Vol. 45, 1997, pp. 457–467.        [ Links ]

10. Parhi K., VLSI Digital Signal Processing Systems Design and Implementation, John Wiley and Sons, New York, 1999.        [ Links ]

11. Samadi S., "Explicit formula for improved filter sharpening polynomial." IEEE Transactions on Signal Processing, Vol. 9, 2000, pp. 2857–2959.        [ Links ]

12. Saramaki T., Y. Neuvo and Mitra S., "Design of Computational Efficient Interpolated FIR Filters," IEEE Trans. on Circuits and Syst., Vol. 35, 1988, pp. 70–88.        [ Links ]

13. Webb J. and D. Munson, "A new approach to designing computationally efficient interpolated FIR filters." IEEE Transactions on Signal Processing, Vol. 44, 1996, pp. 1923–1931.        [ Links ]

Creative Commons License All the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution License