Signal recognition: Fourier transform vs. Hartley transform

被引：2
|
作者：
Gelman, L [1 ]
Sanderson, M [1 ]
Thompson, C [1 ]
机构：
[1] Cranfield Univ, Dept Proc & Syst Engn, PASE, Cranfield MK43 0AL, Beds, England
来源：
PATTERN RECOGNITION | 2003年 / 36卷 / 12期
关键词：
statistical pattern recognition; real and imaginary Fourier components; Hartley transform; Gaussian recognition; likelihood ratio;
D O I：
10.1016/S0031-3203(03)00220-6
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The new generic feature representation approach was utilized for Gaussian recognition. Approach consists of using simultaneously two new recognition features: real and imaginary Fourier components with taking into account the covariance between features. Advanced time-frequency technique, short time Fourier transform was considered. The recognition effectiveness between the new approach and Hartley based approach was compared. It was shown for Gaussian recognition that Hartley approach is not an optimal and is not even a particular case of the proposed approach. The use of the proposed approach provides an essential effectiveness gain in comparison with Hartley approach. (C) 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码：2849 / 2853
页数：5
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