Design and Performance Evaluation of a Deep Neural Network for Spectrum Recognition of Underwater Targets

Liu, Dali; Zhao, Xuchen; Cao, Wen-Jing; Wang, Wei; Lu, Yi

doi:10.1155/2020/8848507

Cited by 10 publications

(3 citation statements)

References 21 publications

(19 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Feng et al [47] proposed a fusion feature and 18-layer residual network method to achieve the classification of underwater targets. Liu et al [48] used a 1D-CNN model to identify the envelope-modulated line spectrum on the DEMON spectrum of underwater target radiation noise with good generalization capability. Yang et al [49] proposed a deep neural network classification system, which can automatically learn more discriminative advanced features in the wavelet packet component energy and then realized the classification and recognition of underwater targets.…”

Section: Underwater Target Radiation Noise Recognition Based On Deep ...mentioning

confidence: 99%

Present status and challenges of underwater acoustic target recognition technology: A review

Lei

Xiaofang²,

Wang³

et al. 2022

Front. Phys.

View full text Add to dashboard Cite

Future naval warfare has placed high demands on underwater targets’ target detection, target recognition, and opposition resistance, among other things. However, the ocean’s complex underwater acoustic environment and the evolving “stealth” technology of underwater targets pose significant challenges to target detection systems, which has become a hot topic in the field of underwater acoustic signal processing in various countries. This study introduced the mechanism of underwater target radiation noise generation, analyzed the research progress and development of underwater target radiation noise recognition by applying machine learning from three perspectives: signal acquisition, feature extraction, and signal recognition at home and abroad, and elaborated on the challenges of underwater target-radiated noise recognition technology against the backdrop of rapid computing science development, and finally, an integrated signal processing method based on the fusion of traditional feature extraction methods and deep learning is proposed for underwater target radiation noise recognition, which improves the low recognition rate of traditional methods and also circumvents the problem of deep learning requiring high computational cost, and is an important direction for future hydroacoustic signal processing.

show abstract

Section: Underwater Target Radiation Noise Recognition Based On Deep ...mentioning

confidence: 99%

Present status and challenges of underwater acoustic target recognition technology: A review

Lei

Xiaofang²,

Wang³

et al. 2022

Front. Phys.

View full text Add to dashboard Cite

show abstract

“…Because the recognition framework of a single neural network makes the extraction of all features of underwater acoustic signals challenging [18][19][20], the research is usually focused on the development of deeper and more complex networks [21][22][23][24][25][26][27][28][29], which however are more difficult to train (in terms of training data size and labeling requirements). Therefore, building a new network model by combining various network structures may be a good solution.…”

Section: Introductionmentioning

confidence: 99%

Underwater acoustic target recognition method based on a joint neural network

et al. 2022

View full text Add to dashboard Cite

To improve the recognition accuracy of underwater acoustic targets by artificial neural network, this study presents a new recognition method that integrates a one-dimensional convolutional neural network and a long short-term memory network. This new network framework is constructed and applied to underwater acoustic target recognition for the first time. Ship acoustic data are used as input to evaluate the network performance. A visual analysis of the recognition results is performed. The results show that this method can realize the recognition and classification of underwater acoustic targets. Compared with a single neural network, the relevant indices, such as the recognition accuracy of the joint network are considerably higher. This provides a new direction for the application of deep learning in the field of underwater acoustic target recognition.

show abstract

“…For instance, statistical models, such as support vector machine (SVM) [5][6][7] and hidden Markov model (HMM) [8,9], have been adopted to facilitate and enhance the pipeline detection process utilizing the various extracted signals from the detection techniques. Liu et al evaluated a deep neural network for spectrum recognition of underwater targets [10]. Sohaib et al compared detection performance between statistic models on the boiler tube [11].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Hidden Markov Model for Pipeline Leakage Detection

Zhang

Chen

2021

Applied Sciences

View full text Add to dashboard Cite

In this paper, a deep neural network hidden Markov model (DNN-HMM) is proposed to detect pipeline leakage location. A long pipeline is divided into several sections and the leakage occurs in different section that is defined as different state of hidden Markov model (HMM). The hybrid HMM, i.e., DNN-HMM, consists of a deep neural network (DNN) with multiple layers to exploit the non-linear data. The DNN is initialized by using a deep belief network (DBN). The DBN is a pre-trained model built by stacking top-down restricted Boltzmann machines (RBM) that compute the emission probabilities for the HMM instead of Gaussian mixture model (GMM). Two comparative studies based on different numbers of states using Gaussian mixture model-hidden Markov model (GMM-HMM) and DNN-HMM are performed. The accuracy of the testing performance between detected state sequence and actual state sequence is measured by micro F1 score. The micro F1 score approaches 0.94 for GMM-HMM method and it is close to 0.95 for DNN-HMM method when the pipeline is divided into three sections. In the experiment that divides the pipeline as five sections, the micro F1 score for GMM-HMM is 0.69, while it approaches 0.96 with DNN-HMM method. The results demonstrate that the DNN-HMM can learn a better model of non-linear data and achieve better performance compared to GMM-HMM method.

show abstract

Design and Performance Evaluation of a Deep Neural Network for Spectrum Recognition of Underwater Targets

Cited by 10 publications

References 21 publications

Present status and challenges of underwater acoustic target recognition technology: A review

Present status and challenges of underwater acoustic target recognition technology: A review

Underwater acoustic target recognition method based on a joint neural network

A Hybrid Hidden Markov Model for Pipeline Leakage Detection

Contact Info

Product

Resources

About