Underwater Acoustical Localization of the Black Box Utilizing Single Autonomous Underwater Vehicle Based on the Second-Order Time Difference of Arrival

Sun, Sibo; Zhang, Xinyu; Zheng, Ce; Fu, Jin; Zhao, Chunhui

doi:10.1109/joe.2019.2950954

Cited by 47 publications

(24 citation statements)

References 25 publications

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“…The considered energy model (Expression ( 1)) relies on the fact that the acoustic source is stationary. Targeting moving sound sources is mostly considered with direction or angle measurements [46,47], combined with microphone arrays [48], or mostly relying on the measurement of propagation time [49], given the achievement of very satisfactory accuracy, despite the complexity of the physical devices employed. While in the space-state domain Kalman filters and maximum a posteriori estimation are commonly used [50], ANNs have also been considered [51,52].…”

Section: Energy-based Acoustic Localizationmentioning

confidence: 99%

A Feed-Forward Neural Network Approach for Energy-Based Acoustic Source Localization

Correia

Tomic

Beko

2021

JSAN

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The localization of an acoustic source has attracted much attention in the scientific community, having been applied in several different real-life applications. At the same time, the use of neural networks in the acoustic source localization problem is not common; hence, this work aims to show their potential use for this field of application. As such, the present work proposes a deep feed-forward neural network for solving the acoustic source localization problem based on energy measurements. Several network typologies are trained with ideal noise-free conditions, which simplifies the usual heavy training process where a low mean squared error is obtained. The networks are implemented, simulated, and compared with conventional algorithms, namely, deterministic and metaheuristic methods, and our results indicate improved performance when noise is added to the measurements. Therefore, the current developed scheme opens up a new horizon for energy-based acoustic localization, a field where machine learning algorithms have not been applied in the past.

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Section: Energy-based Acoustic Localizationmentioning

confidence: 99%

A Feed-Forward Neural Network Approach for Energy-Based Acoustic Source Localization

Correia

Tomic

Beko

2021

JSAN

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“…An algorithm named second-order time difference of arrival (STDOA) has been applied in finding the black box that was proposed in [22]. STDOA is defined as the difference of TDOA and it eliminates the unknown signal period.…”

Section: Related Workmentioning

confidence: 99%

“…The underwater hydrophones have a fixed location known by the localization service. Unlike the black box localization proposed in [22] the target in our paper has a random transmission period and the localization system does not have a pre-information about the transmitted signal. The classic signal structure is mainly a positioning signal with sine and cosine as carriers, but it is not suitable for underwater acoustic channels.…”

Section: System Model Of the Underwater Acoustic Sensors Networkmentioning

confidence: 99%

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Underwater TDOA Acoustical Location Based on Majorization-Minimization Optimization

Sun

Esmaiel

2020

Sensors

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Underwater acoustic localization is a useful technique applied to any military and civilian applications. Among the range-based underwater acoustic localization methods, the time difference of arrival (TDOA) has received much attention because it is easy to implement and relatively less affected by the underwater environment. This paper proposes a TDOA-based localization algorithm for an underwater acoustic sensor network using the maximum-likelihood (ML) ratio criterion. To relax the complexity of the proposed localization complexity, we construct an auxiliary function, and use the majorization-minimization (MM) algorithm to solve it. The proposed localization algorithm proposed in this paper is called a T-MM algorithm. T-MM is applying the MM algorithm to the TDOA acoustic-localization technique. As the MM algorithm iterations are sensitive to the initial points, a gradient-based initial point algorithm is used to set the initial points of the T-MM scheme. The proposed T-MM localization scheme is evaluated based on squared position error bound (SPEB), and through calculation, we get the SPEB expression by the equivalent Fisher information matrix (EFIM). The simulation results show how the proposed T-MM algorithm has better performance and outperforms the state-of-the-art localization algorithms in terms of accuracy and computation complexity even under a high presence of underwater noise.

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“…Different methods, both wired and wireless approaches, for the propagation of these signals underwater exist. These include acoustics [ 8 ], radio frequency (RF) [ 9 ], optical waves [ 10 ], and wired communication [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Design, Implementation, and Measurement Procedure of Underwater and Water Surface Antenna for LoRa Communication

Dala

Arslan

2021

Sensors

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There is an increasing interest in water bodies, which make up more that seventy percent of our planet. It is thus imperative that the water environment should be remotely monitored. Radio frequency (RF) signals have higher bandwidth and lower latency compared to acoustic signals. However, water has high permittivity and conductivity which presents a challenge for the implementation of RF technology. In this work, we undertook a novel design, fabrication, measurement and implementation of an antenna for a sensor node with dual ability of underwater and water surface long range (LoRa) communication at 868 MHz. It was observed that the antenna’s performance deteriorated underwater without −10 dB effective bandwidth between 668 MHz and 1068 MHz. The introduction of an oil-impregnated paper buffer around the antenna resulted in an effective 400 MHz bandwidth within the same frequency span. The sensor node with the buffered antenna was able to achieve a distance of 6 m underwater and 160 m over water surface communication to a data gateway node. The sensor node without the buffered antenna was only able to achieve 80 m over water surface communication. These experimental results show the feasibility of using the LoRa 868 MHz frequency in underwater and water surface communication.

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Underwater Acoustical Localization of the Black Box Utilizing Single Autonomous Underwater Vehicle Based on the Second-Order Time Difference of Arrival

Cited by 47 publications

References 25 publications

A Feed-Forward Neural Network Approach for Energy-Based Acoustic Source Localization

A Feed-Forward Neural Network Approach for Energy-Based Acoustic Source Localization

Underwater TDOA Acoustical Location Based on Majorization-Minimization Optimization

Design, Implementation, and Measurement Procedure of Underwater and Water Surface Antenna for LoRa Communication

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