Underwater Target Recognition Based on Multi-Decision LOFAR Spectrum Enhancement: A Deep-Learning Approach

被引:36
作者
Chen, Jie [1 ]
Han, Bing [1 ]
Ma, Xufeng [1 ]
Zhang, Jian [1 ]
机构
[1] Univ Elect Sci & Technol China, Natl Key Lab Sci & Technol Commun, Chengdu 610054, Peoples R China
关键词
underwater acoustic communication; underwater target recognition; LOFAR spectrum; line spectrum enhancement; deep learning; FEATURE-EXTRACTION; DECOMPOSITION; CNN;
D O I
10.3390/fi13100265
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Underwater target recognition is an important supporting technology for the development of marine resources, which is mainly limited by the purity of feature extraction and the universality of recognition schemes. The low-frequency analysis and recording (LOFAR) spectrum is one of the key features of the underwater target, which can be used for feature extraction. However, the complex underwater environment noise and the extremely low signal-to-noise ratio of the target signal lead to breakpoints in the LOFAR spectrum, which seriously hinders the underwater target recognition. To overcome this issue and to further improve the recognition performance, we adopted a deep-learning approach for underwater target recognition, and a novel LOFAR spectrum enhancement (LSE)-based underwater target-recognition scheme was proposed, which consists of preprocessing, offline training, and online testing. In preprocessing, we specifically design a LOFAR spectrum enhancement based on multi-step decision algorithm to recover the breakpoints in LOFAR spectrum. In offline training, the enhanced LOFAR spectrum is adopted as the input of convolutional neural network (CNN) and a LOFAR-based CNN (LOFAR-CNN) for online recognition is developed. Taking advantage of the powerful capability of CNN in feature extraction, the recognition accuracy can be further improved by the proposed LOFAR-CNN. Finally, extensive simulation results demonstrate that the LOFAR-CNN network can achieve a recognition accuracy of 95.22%, which outperforms the state-of-the-art methods.
引用
收藏
页数:21
相关论文
共 40 条
[1]   Rolling element bearing faults diagnosis based on autocorrelation of optimized: wavelet de-noising technique [J].
Al-Raheem, Khalid F. ;
Roy, Asok ;
Ramachandran, K. P. ;
Harrison, D. K. ;
Grainger, Steven .
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2009, 40 (3-4) :393-402
[2]   A New Feature Extraction Method for Ship-Radiated Noise Based on Improved CEEMDAN, Normalized Mutual Information and Multiscale Improved Permutation Entropy [J].
Chen, Zhe ;
Li, Yaan ;
Cao, Renjie ;
Ali, Wasiq ;
Yu, Jing ;
Liang, Hongtao .
ENTROPY, 2019, 21 (06)
[3]   Sound Event Detection in Underground Parking Garage Using Convolutional Neural Network [J].
Ciaburro, Giuseppe .
BIG DATA AND COGNITIVE COMPUTING, 2020, 4 (03) :1-14
[4]   Improving Smart Cities Safety Using Sound Events Detection Based on Deep Neural Network Algorithms [J].
Ciaburro, Giuseppe ;
Iannace, Gino .
INFORMATICS-BASEL, 2020, 7 (03)
[5]  
Di Martino J. C., 1993, ICASSP-93. 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing (Cat. No.92CH3252-4), P317, DOI 10.1109/ICASSP.1993.319119
[6]  
Goodfellow I, 2016, ADAPT COMPUT MACH LE, P1
[7]   Deep Residual Learning for Image Recognition [J].
He, Kaiming ;
Zhang, Xiangyu ;
Ren, Shaoqing ;
Sun, Jian .
2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2016, :770-778
[8]  
Hou W, 1988, ACTA ACUST, V2, P46
[9]   Deep Learning Methods for Underwater Target Feature Extraction and Recognition [J].
Hu, Gang ;
Wang, Kejun ;
Peng, Yuan ;
Qiu, Mengran ;
Shi, Jianfei ;
Liu, Liangliang .
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2018, 2018
[10]   Feature Extraction for Rolling Element Bearing Faults Using Resonance Sparse Signal Decomposition [J].
Huang, W. ;
Sun, H. ;
Liu, Y. ;
Wang, W. .
EXPERIMENTAL TECHNIQUES, 2017, 41 (03) :251-265