Sonar Data Simulation

Guériot, Didier; Sintes, Christophe

doi:10.5772/18029

Cited by 2 publications

(4 citation statements)

References 23 publications

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“…Real sonar dataset simulation is vital for various tuning, classification, and detection methods relative to atmosphere and collection features. Thus, practical and precise simulation tools play a vital role in enhancing system and algorithm performances, resulting in an adequate number of realistic datasets [37,38]. Different simulation techniques have been used widely.…”

Section: Datasetmentioning

confidence: 99%

“…The more accuracy one applies in selecting the parameters, the realistic images one gets, resulting in predicting algorithms' better performance. One of the critical areas one should focus on while using the simulation tools is a modular and ductile underwater environment illustration, many types of sensors, many atmospheres, and many accumulation conditions [37]. Similarly, since passive sonar data without labels are abundant, [46,47] have conducted studies utilizing unclassified sonar data for pre-training to raise the performance of supervised learning.…”

Section: Datasetmentioning

confidence: 99%

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A Review on Deep Learning-Based Approaches for Automatic Sonar Target Recognition

2020

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Underwater acoustics has been implemented mostly in the field of sound navigation and ranging (SONAR) procedures for submarine communication, the examination of maritime assets and environment surveying, target and object recognition, and measurement and study of acoustic sources in the underwater atmosphere. With the rapid development in science and technology, the advancement in sonar systems has increased, resulting in a decrement in underwater casualties. The sonar signal processing and automatic target recognition using sonar signals or imagery is itself a challenging process. Meanwhile, highly advanced data-driven machine-learning and deep learning-based methods are being implemented for acquiring several types of information from underwater sound data. This paper reviews the recent sonar automatic target recognition, tracking, or detection works using deep learning algorithms. A thorough study of the available works is done, and the operating procedure, results, and other necessary details regarding the data acquisition process, the dataset used, and the information regarding hyper-parameters is presented in this article. This paper will be of great assistance for upcoming scholars to start their work on sonar automatic target recognition.

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Section: Datasetmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

A Review on Deep Learning-Based Approaches for Automatic Sonar Target Recognition

2020

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“…The simulator in [13] uses two methods for sonar imaging: (i) a ray tracing method and (ii) a method that uses the rays to create tubes for modelling the propagation of acoustic waves. When a tube is emitted from a transmitter, it produces a footprint on the surface of the objects.…”

Section: Introductionmentioning

confidence: 99%

“…• Reflectivity: It should consider material properties on the surface of the objects in the scene. Therefore macro and micro-textures similar to that in [13] should be used. It should be able to be adapted or customized to the simulated sonar.…”

Section: Introductionmentioning

confidence: 99%

Imaging sonar simulator for assessment of image registration techniques

Almanza-Medina

Henson

Zakharov

2019

Oceans 2019 MTS/Ieee Seattle

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This work focuses on building a Forward-Looking sonar simulator capable of generating large volumes of ground truth data to test algorithms such as: novel image registration techniques for trajectory estimation, three-dimensional reconstruction or to be used as training data for machine learning algorithms. The simulator is capable of generating realistic data sets of images and providing ground truth data with the exact position and attitude of the sonar related to objects in a test scenario. The sonar simulator is developed using the Unity software platform. This work also shows an example application. Simulated image data sets for different sonar trajectories and seabed textures were created. These data sets are used by an attitude-trajectory estimation method and a quantitative analysis of the method is presented.

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Sonar Data Simulation

Cited by 2 publications

References 23 publications

A Review on Deep Learning-Based Approaches for Automatic Sonar Target Recognition

A Review on Deep Learning-Based Approaches for Automatic Sonar Target Recognition

Imaging sonar simulator for assessment of image registration techniques

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