DOA Estimation of Underwater Acoustic Array Signal Based on Wavelet Transform With Double Branch Convolutional Neural Network

Wang, JingJing; Quan, Tianqi; Jiao, Lulu; Zhang, Weilong; Gulliver, Thomas Aaron; Yang, Xiaomin

doi:10.1109/tvt.2022.3203034

Cited by 6 publications

(2 citation statements)

References 28 publications

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“…Similarly, [27] proposes a CRNN for 2D estimations, while the same model identifies the temporal occurrences of sounds in a 3D spatial context [28]. The CNN competencies in marine configurations are presented in [29] and a simplified CNN is launched in [30] to address mutual coupling in a spherical antenna array. Similarly, [31] relates multi-source estimations to CNNs and sparse arrays, while [32] explores a multiple-input multiple-output system with many targets for a limited range of central bearing angles.…”

Section: Prior Workmentioning

confidence: 99%

Improving DOA Estimation via an Optimal Deep Residual Neural Network Classifier on Uniform Linear Arrays

Kassir,

Kantartzis,

Lazaridis

et al. 2024

IEEE Open J. Antennas Propag.

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The main objective of this work is to improve and evaluate the effectiveness of the neural network (NN) architecture in the domain of estimation of direction of arrival (DOA), with an emphasis on a multi-class classification task with grid resolutions of 0.25 and 0.1. Specifically, a comprehensive assessment is performed to determine the competence of a residual NN (ResNet) in predicting the angle of arrival (AOA) of intercepted signals. Such signals are received by a 16-element uniform linear array and are subjected to real-world noise conditions. To this end, the superiority of the ResNet architecture in DOA estimations is substantiated through a comparison analysis with two other highly recognized NNs, namely, the feed-forward NN and the convolutional NN. Numerical results indicate that the ResNet model exhibits notable precision in estimating the AOAs, across various classes within a broad spectrum, along with a rapid temporal response. Finally, it remains consistent and maintains its superior performance even for diverse incoming signals and significantly reduced SNRs.INDEX TERMS Antenna array analysis and synthesis, antenna optimization, machine learning, Directionof-arrival (DOA) estimation, residual neural networks,

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Section: Prior Workmentioning

confidence: 99%

Improving DOA Estimation via an Optimal Deep Residual Neural Network Classifier on Uniform Linear Arrays

Kassir,

Kantartzis,

Lazaridis

et al. 2024

IEEE Open J. Antennas Propag.

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show abstract

“…At present, the method of underwater DOA estimation via the emerging artificial intelligence technology has certain advantages. Most of them regard DOA estimation as a classification problem [18]. By using the existing network and relying on a single signal feature to achieve DOA estimation, good estimation results can be obtained in the Gaussian noise environments.…”

Section: Introductionmentioning

confidence: 99%

TFF-CNN: time-frequency fusion-based CNN for DOA estimation of underwater acoustic array signal

Zhang,

liu

2023

5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023)

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In the Marine environment, underwater acoustic positioning signals are susceptible to complex underwater acoustic channels and multiple types of underwater noise, resulting in inaccurate DOA estimation, which will significantly reduce the accuracy and reliability of underwater positioning, and the real-time performance of underwater positioning cannot be guaranteed. In this paper, a Time-Frequency fusion based convolutional neural network (TFF-CNN) method for underwater DOA estimation is proposed. In this method, fusion feature maps are constructed based on Time-Frequency characteristics of signals, and a novel convolutional neural network with the deconvolution layer is designed to enhance feature representation and noise resistance. The experimental results show that compared with traditional methods and other networks, the proposed algorithm has great advantages in the estimation accuracy and speed, especially under non-Gaussian noise.

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An underwater target azimuth trajectory enhancement approach in BTR

Zhu,

Yin,

Guo

et al. 2025

Applied Acoustics

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DOA Estimation of Underwater Acoustic Array Signal Based on Wavelet Transform With Double Branch Convolutional Neural Network

Cited by 6 publications

References 28 publications

Improving DOA Estimation via an Optimal Deep Residual Neural Network Classifier on Uniform Linear Arrays

Improving DOA Estimation via an Optimal Deep Residual Neural Network Classifier on Uniform Linear Arrays

TFF-CNN: time-frequency fusion-based CNN for DOA estimation of underwater acoustic array signal

An underwater target azimuth trajectory enhancement approach in BTR

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