Research on underwater sound velocity calculation, error correction and positioning algorithms

Gao, Ruru; Xu, Tianhe; Ai, Qingsong

doi:10.1109/cpgps.2017.8075094

Cited by 7 publications

(3 citation statements)

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“…Underwater acoustic waves travel in an inhomogeneous environment with varying speeds [12]. Gao et al provide a comparison between six different SS estimation algorithms and provide the ranges of temperature, salinity and depths at which each algorithm is applicable [13]. On the other hand, Leroy et al have proposed a unified equation to estimate the SS across all oceans by introducing the latitude [14].…”

Section: Related Workmentioning

confidence: 99%

Acoustic Beam Characterization and Selection for Optimized Underwater Communication

Ahmed

Younis

2019

Applied Sciences

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To increase underwater acoustic signal detectability and conserve energy, nodes leverage directional transmissions. In addition, nodes operate in a three-dimensional (3D) environment that is categorized as inhomogeneous where a propagating signal changes its direction based on the observed sound speed profile (SSP). Coupling 3D directional transmission with frequent node drifts and the varying underwater SSP complicates the process of selecting suitable transmission angles to maintain underwater communication links. Fundamentally, utilizing directional transmission while nodes are drifting causes breaks in established communication links and thus nodes need to find new angles to reestablish these links. Moreover, selecting arbitrary transmission angles may lead to overlapping beams or result in leaving an underwater region uncovered. To tackle the abovementioned challenges, this paper proposes an autonomous beam selection approach that optimizes underwater communication by selecting non-overlapping beams while mitigating the possibility of missing a region, i.e., maximize coverage. Such optimization is achieved by utilizing a structured angle selection mechanism that accounts for the capability of the used transducer. Moreover, we introduce an algorithm suited for resource constrained nodes to classify rays into different types. Then we divide the underwater medium into regions where each region is identified by the limits of the coverage area of each ray type. Finally, we utilize the limits of these regions to aid nodes in selecting the best ray to reestablish communication with drifted nodes. We validate our contribution through simulation where actual SSPs are leveraged to validate the beam classification process.

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Section: Related Workmentioning

confidence: 99%

Acoustic Beam Characterization and Selection for Optimized Underwater Communication

Ahmed

Younis

2019

Applied Sciences

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“…The traditional method of solving the problem of sound ray bending is based on indirect measurement. The indirect method is used to calculate the sound velocity of sea water, that is, the sound velocity is calculated by the sound velocity empirical model according to the depth, temperature and salinity [7]. In addition, some scholars also use many types of sound velocity models, including Dell Grosso, Leroy, Mackenzie and other 6 models which are introduced and compared in [7].…”

Section: Introductionmentioning

confidence: 99%

“…The indirect method is used to calculate the sound velocity of sea water, that is, the sound velocity is calculated by the sound velocity empirical model according to the depth, temperature and salinity [7]. In addition, some scholars also use many types of sound velocity models, including Dell Grosso, Leroy, Mackenzie and other 6 models which are introduced and compared in [7]. DNNs is applied for source localization in a shallow water environment and the results demonstrate that DNNs are effective for source localization in complex and varied water environments, especially when there is little precise environmental information [8].…”

Section: Introductionmentioning

confidence: 99%

Node Selection Algorithm for Underwater Acoustic Sensor Network Based on Particle Swarm Optimization

Cheng

Yuan

et al. 2019

IEEE Access

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Underwater target positioning technology is the most important part of UnderWater Acoustic Sensor Network(called UWASN), and it is one of the most important research directions in this field with broad application prospects in commercial and military fields. Due to the complex and variability of underwater acoustic environment, the underwater acoustic sensor network has the characteristics of fluidity, sparse deployment and energy limitation, which brings certain challenges to underwater positioning technology. Aiming at the scenario that the node redundancy in the underwater acoustic sensor network leads to low positioning efficiency, this paper considers the sound velocity correction factor based on the traditional anchor node selection algorithm in this paper. Under the premise of ensuring certain positioning accuracy, considering the communication overhead, node residual energy, position suspiciousness, sound ray propagation bending characteristics and other factors, the anchor node optimization mechanism which uses the particle swarm algorithm to iterate out the optimal sensor combination for improving the accuracy of positioning is designed. The simulation results show that the proposed algorithm shows small calculation, fast convergence and high positioning accuracy. It can effectively improve the energy utilization of nodes, balance positioning performance as well as energy use efficiency, and optimize the positioning result of UWASN, which is well suited for underwater acoustic positioning scenarios. INDEX TERMS UWASN, node selection, sound velocity correction, underwater acoustic positioning.

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A continuous-wave method for sound speed measurement based on an infinite-echo model

Salvi

Tan

2022

Measurement

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Research on underwater sound velocity calculation, error correction and positioning algorithms

Cited by 7 publications

References 0 publications

Acoustic Beam Characterization and Selection for Optimized Underwater Communication

Acoustic Beam Characterization and Selection for Optimized Underwater Communication

Node Selection Algorithm for Underwater Acoustic Sensor Network Based on Particle Swarm Optimization

A continuous-wave method for sound speed measurement based on an infinite-echo model

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