Machine and deep learning approaches to localization and range estimation of underwater acoustic sources

Houégnigan, Ludwig; Safari, Pooyan; Nadeu, Climent; André, M.; Schaar, Mike van der

doi:10.1109/rioacoustics.2017.8349716

Cited by 11 publications

(5 citation statements)

References 6 publications

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“…In 2017, Houégnigan et al [39] pointed out that, although underwater range can be standardly estimated by widely spaced sensors in the higher frequency ranges and assuming direct path, opportunistically estimated using surface and bottom reflection or using modal decomposition at certain low frequencies, and it remains a big challenge to develop a general system based on a single sensor or a smallaperture array that can adapted to real time. ey introduced the early results of their ongoing underwater localization and sound source range estimation based on a single sensor and the experimental results of range estimation using shallow and deep neural networks by a single sensor.…”

Section: Wireless Outdoor Localization Technology Based On Deep Learningmentioning

confidence: 99%

“…ey introduced the early results of their ongoing underwater localization and sound source range estimation based on a single sensor and the experimental results of range estimation using shallow and deep neural networks by a single sensor. e deep neural networks used in [39] are AlexNet, VGG-16, and VGG-19.…”

Section: Wireless Outdoor Localization Technology Based On Deep Learningmentioning

confidence: 99%

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Wireless Localization Based on Deep Learning: State of Art and Challenges

Ye¹,

Lu²,

You³

et al. 2020

Mathematical Problems in Engineering

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The problem of position estimation has always been widely discussed in the field of wireless communication. In recent years, deep learning technology is rapidly developing and attracting numerous applications. The high-dimension modeling capability of deep learning makes it possible to solve the localization problems under many nonideal scenarios which are hard to handle by classical models. Consequently, wireless localization based on deep learning has attracted extensive research during the last decade. The research and applications on wireless localization technology based on deep learning are reviewed in this paper. Typical deep learning models are summarized with emphasis on their inputs, outputs, and localization methods. Technical details helpful for enhancing localization ability are also mentioned. Finally, some problems worth further research are discussed.

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Section: Wireless Outdoor Localization Technology Based On Deep Learningmentioning

confidence: 99%

Section: Wireless Outdoor Localization Technology Based On Deep Learningmentioning

confidence: 99%

Wireless Localization Based on Deep Learning: State of Art and Challenges

Ye¹,

Lu²,

You³

et al. 2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…Due to the success of deep neural networks (DNN) in other domains, data-driven (DD) methods have been recently considered more extensively as potentially viable solutions to the UWA localization problem [8][9][10][11][12][13][14][15][16]. Specifically, a DD variant of the statistically superior direct localization (DLOC) approach [17,18] has been proposed in [19].…”

Section: Introductionmentioning

confidence: 99%

Towards Robust Data-Driven Underwater Acoustic Localization: A Deep CNN Solution with Performance Guarantees for Model Mismatch

Weiss

Singer

Wornell

2023

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Key challenges in developing underwater acoustic localization methods are related to the combined effects of high reverberation in intricate environments. To address such challenges, recent studies have shown that with a properly designed architecture, neural networks can lead to unprecedented localization capabilities and enhanced accuracy. However, the robustness of such methods to environmental mismatch is typically hard to characterize, and is usually assessed only empirically. In this work, we consider the recently proposed data-driven method [19] based on a deep convolutional neural network, and demonstrate that it can learn to localize in complex and mismatched environments. To explain this robustness, we provide an upper bound on the localization mean squared error (MSE) in the "true" environment, in terms of the MSE in a "presumed" environment and an additional penalty term related to the environmental discrepancy. Our theoretical results are corroborated via simulation results in a rich, highly reverberant, and mismatch channel.

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“…Meanwhile, deep learning technology [13] developed rapidly and had achieved effects comparable to humans in image recognition [14], speech, and natural language processing [15,16]. Deep neural networks, such as convolutional neural networks (CNN) and long short-term memory (LSTM) [17], have a high degree of nonlinearity and information fusion capabilities. Deep learning technology provides new solutions to solve the localization problem in complex scenarios.…”

Section: Introductionmentioning

confidence: 99%

SL-BiLSTM: A Signal-Based Bidirectional LSTM Network for Over-the-Horizon Target Localization

Wang

et al. 2021

Mathematical Problems in Engineering

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Deep learning technology provides novel solutions for localization in complex scenarios. Conventional methods generally suffer from performance loss in the long-distance over-the-horizon (OTH) scenario due to uncertain ionospheric conditions. To overcome the adverse effects of the unknown and complex ionosphere on positioning, we propose a deep learning positioning method based on multistation received signals and bidirectional long short-term memory (BiLSTM) network framework (SL-BiLSTM), which refines position information from signal data. Specifically, we first obtain the form of the network input by constructing the received signal model. Second, the proposed method is developed to predict target positions using an SL-BiLSTM network, consisting of three BiLSTM layers, a maxout layer, a fully connected layer, and a regression layer. Then, we discuss two regularization techniques of dropout and randomization which are mainly adopted to prevent network overfitting. Simulations of OTH localization are conducted to examine the performance. The parameters of the network have been trained properly according to the scenario. Finally, the experimental results show that the proposed method can significantly improve the accuracy of OTH positioning at low SNR. When the number of training locations increases to 200, the positioning result of SL-BiLSTM is closest to CRLB at high SNR.

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Machine and deep learning approaches to localization and range estimation of underwater acoustic sources

Cited by 11 publications

References 6 publications

Wireless Localization Based on Deep Learning: State of Art and Challenges

Wireless Localization Based on Deep Learning: State of Art and Challenges

Towards Robust Data-Driven Underwater Acoustic Localization: A Deep CNN Solution with Performance Guarantees for Model Mismatch

SL-BiLSTM: A Signal-Based Bidirectional LSTM Network for Over-the-Horizon Target Localization

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