顾及行程时间的水下声学定位分段指数随机模型

Segmented Exponential Stochastic Model for Underwater Acoustic Positioning Based on Travel Time

  • 摘要: 高精度的水下定位需要准确且符合实际的随机模型,目前常使用等权和顾及声线入射角的水下定位随机模型,但在水下声学定位过程中初始入射角是未知的。我们分析了入射角扰动、声速扰动及深度对声线传播的影响,并依据入射角扰动引起的声线传播误差与行程时间之间的关系,构建了基于行程时间的分段指数随机模型。通过仿真实验和实测数据进行了验证。结果表明: 1)在相同的深度下,依据不同入射角扰动量级构建的权矩阵具有很好的稳定性; 2)在仿真实验中本模型的定位精度比等权模型、入射角指数模型和入射角余弦模型分别提高了0.169m、0.107m、0.024m,有效抑制了误差,本模型与入射角分段余弦模型和入射角分段指数模型定位精度相当,但本模型保留了更多的观测信息,并在实测数据中进一步得到了验证。

     

    Abstract: Objectives: High-precision underwater positioning requires accurate and realistic stochastic model. Currently, equal-weight stochastic model and stochastic model based on sound ray incidence angle are commonly used. However, the initial incidence angle is often unknown in underwater acoustic positioning, which limits the accuracy of stochastic models. Methods: To construct an accurate and realistic stochastic model, this research proposes a segmented exponential stochastic model that takes into account the travel time of sound ray propagation. We analyzed the effects of incidence angle perturbation, sound speed perturbation, and depth on sound ray propagation. Based on the response relationship between the variance of ranging perturbation and the travel time of sound ray propagation, we utilized a segmented exponential function to fit the variance of ranging perturbation, ultimately achieving the construction of the new stochastic model. Results: The model proposed is validated through simulation data and practical data. The results demonstrate that: 1) At the same depth, the weight matrix constructed based on different incidence angle perturbation exhibits excellent stability; 2) In the simulation data, the positioning precision of the model proposed improved by 0.169m, 0.107m and 0.024m compared to the equal-weight model, the incident angle exponent model and the incident angle cosine model, respectively. The positioning precision of the model proposed is comparable to that of the incident angle segmented cosine model and the incident angle segmented exponent model, but the model proposed retains more observational information. The proposed model has been further validated by practical data. Conclusions: The segmented exponential stochastic model proposed significantly improves positioning precision. This research provides new insights for the development of high-precision underwater positioning technology and has important application value in fields such as marine resource exploration, underwater target detection, and underwater navigation.

     

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