2010Unpublished venueRequires access

A new window function for signal spectrum analysis and FIR filter design

Mahdi Mottaghi-Kashtiban, Mahrokh G. Shayesteh

Open publisher page 18 citations

Abstract

A new window function is presented which like the well known Hamming window offers a preferred property for use in signal spectrum analysis: the sum of window coefficients with its shifted version by half of the order (50% overlap) is constant for the overlapped region in the time domain. In high orders, the new window has main-lobe width equal to Hamming window, while featuring 2~4 dB smaller maximum side-lobe peak. For low orders, the window parameters are modified to have smaller main-lobe width compared to Hamming window, while maintaining smaller maximum side-lobe peak. Our results indicate performance improvement of the proposed window compared to Kaiser and Gaussian windows. A comparison with Dolph-Chebyshev window is also presented. The FIR filters designed by windowing method show the efficiency of the new window.

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What this paper is about

A new window function is presented which like the well known Hamming window offers a preferred property for use in signal spectrum analysis: the sum of window coefficients with its shifted version by half of the order (50% overlap) is constant for the overlapped region in the time domain. In high orders, the new window has main-lobe width equal to Hamming window, while featuring 2~4 dB smaller maximum side-lobe peak. For low orders, the window parameters are modified to have smaller main-lobe width compared to Hamming window, while maintaining smaller maximum side-lobe peak. Our results indicate performance improvement of the proposed window compared to Kaiser and Gaussian windows. A comparison with Dolph-Chebyshev window is also presented. The FIR filters designed by windowing method show the efficiency of the new window.

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Available abstract

A new window function is presented which like the well known Hamming window offers a preferred property for use in signal spectrum analysis: the sum of window coefficients with its shifted version by half of the order (50% overlap) is constant for the overlapped region in the time domain. In high orders, the new window has main-lobe width equal to Hamming window, while featuring 2~4 dB smaller maximum side-lobe peak. For low orders, the window parameters are modified to have smaller main-lobe width compared to Hamming window, while maintaining smaller maximum side-lobe peak. Our results indicate performance improvement of the proposed window compared to Kaiser and Gaussian windows. A comparison with Dolph-Chebyshev window is also presented. The FIR filters designed by windowing method show the efficiency of the new window.

Key concepts: Window function, Side lobe, Main lobe, Hamming code, Window (computing), Finite impulse response, Gaussian, Algorithm

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