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

Abstract

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.

Underwater Acoustic Target Recognition in Passive Sonar Using Spectrogram and Modified MobileNet Network Classifier

“…They achieved 92.64% accuracy in this method. Williams [12] and Galusha et al [13] targets, which considered the spectrum obtained from the low-frequency analysis recording [18]. The proposed LOFAR-CNN method has been able to achieve a recognition accuracy of 22.95%.…”

Underwater Acoustic Target Recognition Using Spectrogram ROI Approximation with Mobilenet One-dimensional and Two-dimensional Networks

“…The average classification recognition rate when tested on a set of acoustic signals reaches 90.9%. (Chen, et al, 2021) proposed a method for detecting underwater acoustic targets, which considered the spectrum obtained from the low-frequency analysis recording as the input of a convolutional neural network (CNN), and developed a LOFAR-based CNN for online detection. The proposed LOFAR-CNN method has been able to achieve a recognition accuracy of 95.22%.…”

Underwater acoustic target recognition based on automatic feature and contrastive coding

“…(1) Feature based on power or energy, such as Power Spectral Density (PSD) [1] and Cyclo‐stationarity [2]. (2) Feature based on time‐frequency analysis, including LOw Frequency Analysis and Recording [3], Wavelet Analysis [4], Wigner‐Ville Distribution [5] and any other features. (3) Feature based on human auditory characteristics, such as the Mel Spectrum [6], Mel‐Frequency Cepstral Coefficients [7] and Gammatone‐Frequency Cepstral Coefficients [8].…”