Noise detection and feature extraction method for underwater vehicle engine speedup radiated noise

2016 ◽  
Author(s):  
Xiangdong Jiang
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Underwater Vehicle ◽  
Noise Detection ◽  
Feature Extraction Method ◽  
Radiated Noise ◽  
Vehicle Engine

Modeling and Feature Extraction from the Noise of Underwater Vehicle Propeller Startup

2013 ◽  
Vol 756-759 ◽  
pp. 4487-4491
Author(s):  
Xiang Dong Jiang ◽  
Fu Li Huang ◽  
Jian Sheng Tang ◽  
Yue Pan
Keyword(s):  
Feature Extraction ◽  
Hough Transform ◽  
Extraction Method ◽  
Underwater Vehicle ◽  
Noise Model ◽  
Feature Extraction Method ◽  
Frequency Space ◽  
Time Frequency ◽  
Vehicle Engine ◽  
Result Show

A noise model of underwater vehicle engine startup is proposed in this paper. Based on this model, a feature extraction method for time variety spectrum of engine startup is introduced. This method using hough transform to cumulate the points of the same spectrum lines in time-frequency space and detect the peaks in transform space. Simulation and data processing result show that the method can estimate the parameter of spectrum lines precisely.

scholarly journals A New Feature Extraction Method for Ship-Radiated Noise Based on Improved CEEMDAN, Normalized Mutual Information and Multiscale Improved Permutation Entropy

Entropy ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. 624 ◽  
Author(s):  
Zhe Chen ◽  
Yaan Li ◽  
Renjie Cao ◽  
Wasiq Ali ◽  
Jing Yu ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Mutual Information ◽  
Noise Reduction ◽  
Extraction Method ◽  
Recognition Rate ◽  
Permutation Entropy ◽  
Feature Extraction Method ◽  
Normalized Mutual Information ◽  
Radiated Noise ◽  
New Feature

Extracting useful features from ship-radiated noise can improve the performance of passive sonar. The entropy feature is an important supplement to existing technologies for ship classification. However, the existing entropy feature extraction methods for ship-radiated noise are less reliable under noisy conditions because they lack noise reduction procedures or are single-scale based. In order to simultaneously solve these problems, a new feature extraction method is proposed based on improved complementary ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), normalized mutual information (norMI), and multiscale improved permutation entropy (MIPE). Firstly, the ICEEMDAN is utilized to obtain a group of intrinsic mode functions (IMFs) from ship-radiated noise. The noise reduction process is then conducted by identifying and eliminating the noise IMFs. Next, the norMI and MIPE of the signal-dominant IMFs are calculated, respectively; and the norMI is used to weigh the corresponding MIPE result. The multi-scale entropy feature is finally defined as the sum of the weighted MIPE results. Experimental results show that the recognition rate of the proposed method achieves 90.67% and 83%, respectively, under noise free and 5 dB conditions, which is much higher than existing entropy feature extraction algorithms. Hence, the proposed method is more reliable and suitable for feature extraction of ship-radiated noise in practice.

scholarly journals Feature Extraction of Ship-Radiated Noise Based on Enhanced Variational Mode Decomposition, Normalized Correlation Coefficient and Permutation Entropy

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 468 ◽  
Author(s):  
Dongri Xie ◽  
Hamada Esmaiel ◽  
Haixin Sun ◽  
Jie Qi ◽  
Zeyad A. H. Qasem
Keyword(s):  
Feature Extraction ◽  
Correlation Coefficient ◽  
Extraction Method ◽  
Recognition Rate ◽  
Permutation Entropy ◽  
Support Vector ◽  
Variational Mode Decomposition ◽  
Feature Extraction Method ◽  
Radiated Noise ◽  
Mode Decomposition

Due to the complexity and variability of underwater acoustic channels, ship-radiated noise (SRN) detected using the passive sonar is prone to be distorted. The entropy-based feature extraction method can improve this situation, to some extent. However, it is impractical to directly extract the entropy feature for the detected SRN signals. In addition, the existing conventional methods have a lack of suitable de-noising processing under the presence of marine environmental noise. To this end, this paper proposes a novel feature extraction method based on enhanced variational mode decomposition (EVMD), normalized correlation coefficient (norCC), permutation entropy (PE), and the particle swarm optimization-based support vector machine (PSO-SVM). Firstly, EVMD is utilized to obtain a group of intrinsic mode functions (IMFs) from the SRN signals. The noise-dominant IMFs are then eliminated by a de-noising processing prior to PE calculation. Next, the correlation coefficient between each signal-dominant IMF and the raw signal and PE of each signal-dominant IMF are calculated, respectively. After this, the norCC is used to weigh the corresponding PE and the sum of these weighted PE is considered as the final feature parameter. Finally, the feature vectors are fed into the PSO-SVM multi-class classifier to classify the SRN samples. The experimental results demonstrate that the recognition rate of the proposed methodology is up to 100%, which is much higher than the currently existing methods. Hence, the method proposed in this paper is more suitable for the feature extraction of SRN signals.

scholarly journals Double Feature Extraction Method of Ship-Radiated Noise Signal Based on Slope Entropy and Permutation Entropy

Entropy ◽  
2021 ◽  
Vol 24 (1) ◽  
pp. 22
Author(s):  
Yuxing Li ◽  
Peiyuan Gao ◽  
Bingzhao Tang ◽  
Yingmin Yi ◽  
Jianjun Zhang
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Nearest Neighbor ◽  
Recognition Rate ◽  
Noise Signal ◽  
Permutation Entropy ◽  
K Nearest Neighbor ◽  
Feature Extraction Method ◽  
Radiated Noise ◽  
Single Feature

In order to accurately identify various types of ships and develop coastal defenses, a single feature extraction method based on slope entropy (SlEn) and a double feature extraction method based on SlEn combined with permutation entropy (SlEn&PE) are proposed. Firstly, SlEn is used for the feature extraction of ship-radiated noise signal (SNS) compared with permutation entropy (PE), dispersion entropy (DE), fluctuation dispersion entropy (FDE), and reverse dispersion entropy (RDE), so that the effectiveness of SlEn is verified, and SlEn has the highest recognition rate calculated by the k-Nearest Neighbor (KNN) algorithm. Secondly, SlEn is combined with PE, DE, FDE, and RDE, respectively, to extract the feature of SNS for a higher recognition rate, and SlEn&PE has the highest recognition rate after the calculation of the KNN algorithm. Lastly, the recognition rates of SlEn and SlEn&PE are compared, and the recognition rates of SlEn&PE are higher than SlEn by 4.22%. Therefore, the double feature extraction method proposed in this paper is more effective in the application of ship type recognition.

scholarly journals A Comparative Study of Multiscale Sample Entropy and Hierarchical Entropy and Its Application in Feature Extraction for Ship-Radiated Noise

Entropy ◽  
2019 ◽  
Vol 21 (8) ◽  
pp. 793 ◽  
Author(s):  
Weijia Li ◽  
Xiaohong Shen ◽  
Yaan Li
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Probabilistic Neural Network ◽  
Extraction Methods ◽  
Sample Entropy ◽  
Real World Data ◽  
Feature Extraction Method ◽  
Radiated Noise ◽  
Frequency Components ◽  
The Difference

The presence of marine ambient noise makes it difficult to extract effective features from ship-radiated noise. Traditional feature extraction methods based on the Fourier transform or wavelets are limited in such a complex ocean environment. Recently, entropy-based methods have been proven to have many advantages compared with traditional methods. In this paper, we propose a novel feature extraction method for ship-radiated noise based on hierarchical entropy (HE). Compared with the traditional entropy, namely multiscale sample entropy (MSE), which only considers information carried in the lower frequency components, HE takes into account both lower and higher frequency components of signals. We illustrate the different properties of HE and MSE by testing them on simulation signals. The results show that HE has better performance than MSE, especially when the difference in signals is mainly focused on higher frequency components. Furthermore, experiments on real-world data of five types of ship-radiated noise are conducted. A probabilistic neural network is employed to evaluate the performance of the obtained features. Results show that HE has a higher classification accuracy for the five types of ship-radiated noise compared with MSE. This indicates that the HE-based feature extraction method could be used to identify ships in the field of underwater acoustic signal processing.

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