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Principal Component Analysis Method and Its Application in Data Noise Reducing
Author(s): 
Pages: 55-58
Year: Issue:  9
Journal: Ordnance Industry Automation

Keyword:  principal component analysiswhite noisede-noising;
Abstract: For acquiring useful signal from acquired signal with noise, ensuring the resultsdata accuracy in the aircraft test, the principal component analysis is proposed to extract the useful signal. Firstly, the fundamental principle of principal component analysis is discussed, and then its relation with singular value decomposition (SVD) is illustrated. Put forward 2 methods using principal component, one uses Hankel matrix and the other uses none repeated matrix, in the noise reduction for single queue signal. 3 types of signals are taken as inputs for the verify simulation and discussion, which are the signal with no trend, the signal with trend and the signal with an impact component. Results show that this method works well in white noise reducing with signal with no trend and signal with trend. It works not so well for signal containing impact component. This method can be referenced for signal processing engineers.
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