# Underwater Target Recognition Based on Multi-Decision
###### 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 communication; underwater target recognition; LOFAR spectrum; line spectrum enhancement; deep learning +
Footnote †: journal: _Future Internet_  
The application of deep learning in the field of underwater target recognition mainly involves three aspects. The first is the field of underwater recognition. Due to many reasons such as confidentiality and security, the collection and production of data sets are difficult. Therefore, researchers will use as many existing samples as possible, such as using Generative Adversarial Networks (GAN) to achieve sample expansion to meet the needs of deep learning with large data volumes. The second is the orthodox field of deep learning, such as computer vision and natural language recognition. Researchers start from optimizing and designing complex deep neural network structures, and only rely on neural networks to complete feature extraction. The third is the data preprocessing stage before inputting the neural network. In view of the serious pollution of the collected data by environmental noise, researchers perform denoising and spectral transformation on audio samples, or perform image denoising on sonar images. Its purpose is to make the sample features as obvious as possible through feature engineering, which is more conducive to the needs of deep neural network feature extraction. In this paper, we are interested in underwater target-recognition methods based on deep learning. The Low-frequency analysis and recording (LOFAR) spectrum is widely used in the field of passive sonar ship target recognition due to its significant sound source information and relatively high signal-to-noise ratio. It transforms the signal from the time domain to the time-frequency domain. Sonars usually observe the line spectrum in the LOFAR spectrum to determine whether the target exists, and perform tracking and recognition. This type of method is mainly realized by extracting features and training classifiers. Unfortunately, because the data are always contaminated by environmental noise, breakpoints are introduced in the LOFAR spectrum, which reduces the performance of signal processing. To overcome this problem and further improve the performance of underwater target recognition, we use deep-learning methods for underwater target recognition, and propose an underwater target-recognition scheme based on LOFAR spectral enhancement (LSE).