Comparative study of adaptive techniques for denoising CN Tower lightning current derivative signals

Authors:
O. Nedjah;A. M. Hussein;S. Krishnan;R. Sotudeh
Affiliations:
Electrical and Computer Engineering Department, Ryerson University, Toronto, Canada and Communication and Electrical Engineering, Hertfordshire University, Hatfield, UK;Electrical and Computer Engineering Department, Ryerson University, Toronto, Canada;Electrical and Computer Engineering Department, Ryerson University, Toronto, Canada;Communication and Electrical Engineering, Hertfordshire University, Hatfield, UK
Venue:
Digital Signal Processing
Year:
2010

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Abstract

The lightning current derivative data recorded at the CN Tower during the past 18 years contain different kinds of noise and needs to be denoised for accurately determining the lightning current waveform parameters. It is usually a challenging task to denoise transient signals having large bandwidth without altering their waveshapes or shrinking their amplitudes. This paper deals with denoising the CN Tower lightning current derivative signals using several adaptive techniques. A new adaptive denoising approach (Divide-and-Conquer) has been successfully used to denoise a vast variety of CN Tower lightning current derivative waveshapes. The supremacy of the new technique over the existing ones is outlined for a signal with a poor signal-to-noise ratio (SNR). While keeping the signal amplitude unchanged and preserving its waveshape, the new denoising technique improved its SNR from -22.93 dB to 71.41 dB.