<p>Spatio-temporal variation of sound speed, in seafloor geodetic precise positioning, can always be attributed to the time error. Firstly, this paper analyzes the existing error compensation model, i.e., the time ratio model, which is expressed by the recorded time multiplying a ratio coefficient. And then a time split model is proposed by expressing the acoustic ray traveling time as the recorded time pluses a perturbation time error. The theoretical differences between the proposed time bias compensation model and the time ratio model are analyzed. Under the new framework, sound speed perturbation models with optimal single-layer spatial gradient and multi-layer spatial gradients are developed to compensate for sound speed error in the complex cases. Numerical computation shows that the simple time split model keeps the same accuracy as some complicated models while considering the distribution of random error. Furthermore, multi-layer model can improve the positioning accuracy without putting the pressure on parametrization.</p>

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Time bias compensation model considering multi-layer sound speed perturbation for underwater acoustic positioning

  • Yuanxi Yang,
  • Shengqiu Zhang,
  • Shuqiang Xue

摘要

Spatio-temporal variation of sound speed, in seafloor geodetic precise positioning, can always be attributed to the time error. Firstly, this paper analyzes the existing error compensation model, i.e., the time ratio model, which is expressed by the recorded time multiplying a ratio coefficient. And then a time split model is proposed by expressing the acoustic ray traveling time as the recorded time pluses a perturbation time error. The theoretical differences between the proposed time bias compensation model and the time ratio model are analyzed. Under the new framework, sound speed perturbation models with optimal single-layer spatial gradient and multi-layer spatial gradients are developed to compensate for sound speed error in the complex cases. Numerical computation shows that the simple time split model keeps the same accuracy as some complicated models while considering the distribution of random error. Furthermore, multi-layer model can improve the positioning accuracy without putting the pressure on parametrization.