期刊名称：International Journal of Electronics Communication and Computer Engineering
印刷版ISSN：2249-071X
电子版ISSN：2278-4209
出版年度：2014
卷号：5
期号：6
页码：1366-1370
出版社：IJECCE
摘要：This paper introduce a new data SONAR classification method based on Short-Time Fractional Fourier Transform (STFrFT) analysis. The passive SONAR system receives the acoustic signals radiated by vessels and attempts to categorize them as a function of the similarities between vessels of the same class.Here, a time-frequency processing and feature extraction method is developed in order to improve the performance of a feedforwardneural network, which is used to classify five classes of vessels.Processing of time-varying signals in fractional fourier domain allows us to estimate the signal with higher concentration than conventional fourier domain, making the technique robust against additive noise, maintaining same computational complexity. With the purpose of dimension reduction and classification improvement, we use Linear Discriminant Analysis (LDA) technique. The feasibility of the proposed technique (STFrFTLDA) has been tested experimentally using a real database. The experimental results show the superiority of the proposed method
关键词：Sonar;Short Time Fractional Fourier Transform;Feature Extraction;Time-Frequency Analysis
