2013Unpublished venueRequires access

Grouped B-spline windows for power spectral density estimation

Lucian Stanciu, Cristian Stanciu

Open publisher page 4 citations

Abstract

The power spectral density of a signal can be estimated most accurately by using a window that can attain a narrower main lobe width and a higher decay rate. Conventional windows are able to control these characteristics by only one parameter, so there is a trade-off problem. To reduce the effect of spectral leakage, a window function can be used whose amplitude tapers smoothly and gradually toward zero at both ends. The spectral methods are accurate for smooth functions and we propose using grouped B-spline windows with three control parameters for power spectral density estimation and analyze its characteristics.

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What this paper is about

The power spectral density of a signal can be estimated most accurately by using a window that can attain a narrower main lobe width and a higher decay rate. Conventional windows are able to control these characteristics by only one parameter, so there is a trade-off problem. To reduce the effect of spectral leakage, a window function can be used whose amplitude tapers smoothly and gradually toward zero at both ends. The spectral methods are accurate for smooth functions and we propose using grouped B-spline windows with three control parameters for power spectral density estimation and analyze its characteristics.

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Available abstract

The power spectral density of a signal can be estimated most accurately by using a window that can attain a narrower main lobe width and a higher decay rate. Conventional windows are able to control these characteristics by only one parameter, so there is a trade-off problem. To reduce the effect of spectral leakage, a window function can be used whose amplitude tapers smoothly and gradually toward zero at both ends. The spectral methods are accurate for smooth functions and we propose using grouped B-spline windows with three control parameters for power spectral density estimation and analyze its characteristics.

Key concepts: Spectral density, Spectral leakage, Window function, Spectral density estimation, Spline (mechanical), Amplitude, Side lobe, B-spline

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