Removing Noise from Noisy Signal Data within Principal Component Analysis Framework

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Düzce Üniversitesi Bilim ve Teknoloji Dergisi
Cilt: 13 Sayı: 3
Removing Noise from Noisy Signal Data within Principal Component Analysis Framework

Removing Noise from Noisy Signal Data within Principal Component Analysis Framework

Pages : 1371-1384

View : 140 | Download : 133

Publication Date : 2025-07-31

Article Type : Research Paper

Abstract :The separation of noise from data represents one of the fundamental problems in signal processing. Principal component analysis (PCA) is a multivariate statistical technique that is employed in all scientific disciplines for the identification of patterns in data and the compression of data by reducing the size without significant loss of information. This paper concerns the removal of noise from noisy sinusoidal data using PCA. The aim is to achieve this by focusing on the separation of noise from signal data without estimating the parameters of sinusoidal signals. To this end, a code was developed in the Mathematica programming language, with modifications of its algorithm then being assessed on data derived from a number of noisy signals. The effectiveness of PCA was assessed by using the mean square error (MSE) values in relation to the variation in signal-to-noise ratio (SNR). The simulation results obtained demonstrate the effectiveness of PCA in removing noise from noisy sinusoidal signals.
Keywords : Temel bileşenler analizi, sinüzoidal, boyut azaltma, optimizasyon

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