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Extraction and classification recognition of ship radiated noise characteristics based on higher-order statistics

Author: ZhangYiJun
Tutor: WangHaiYan
School: Northwestern Polytechnical University
Course: Mechanical and Electronic Engineering
Keywords: Higher order cumulants Order cyclic cumulants 1 ( 1 / 2 ) -dimensional spectrum 2 ( 1 / 2 ) -dimensional spectral phase coupling B-P neural networks Target Identification and Classification
CLC: TP14
Type: Master's thesis
Year: 2001
Downloads: 401
Quote: 7
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Abstract


Higher-order statistics is an effective tool to study the nonlinear and non - Gaussian signal , it has unique advantages in signal detection , feature extraction and harmonic retrieval . This paper application of higher-order statistics feature extraction and classification and identification of underwater targets . The paper first analyzes the physical properties of the ship radiated noise , made ??a variety of extraction methods based on the the ship noise characteristic parameters of the higher-order statistics : the bispectrum and trispectrum analysis , 1 ( 1 / 2 ) -dimensional spectral spectrum analysis , 2 ( 1 / 2) -dimensional spectral phase coupling characteristics analysis , and the first order cyclic cumulant method used in ship radiated noise modulation signal feature extraction: the third-order cycle cumulant diagonal slice spectrum extracted noise periodic component . Line continuous spectral characteristics extracted by the above method as characteristic parameters , using BP neural network to identify the classification of the three types of ship noise sample , the average correct recognition rate reached 91.5% . And verify the correctness and validity of the feature extraction using higher-order statistics . This paper studies suggest that the ship noise modulation information contains a large number of ships, the characteristic parameters ( speed, host type , operating conditions , etc.) , the use of high-end cycle cumulative amount extracted modulation information with other methods ( power cepstrum ) compared has many advantages , such as the inhibition of any smooth ( non-stationary ) Gaussian noise, a separable smooth and non-stationary signals etc. , and to get a better identification of the classification results . As the papers workload , this identification classification , denoising and feature parameters selected research needs to be further explored.

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