The aim of this study is to address the issue of TDOA/FDOA measurement accuracy in complex underwater environments, which is affected by multipath effects and variations in water sound velocity induced by the challenging nature of the underwater environment. To this end, a novel cooperative localisation algorithm has been developed, integrating the attention mechanism and convolutional neural network-bidirectional gated recurrent unit (CNN-BiGRU) with TDOA/FDOA and two-step weighted least squares (ImTSWLS). This algorithm is designed to enhance the accuracy of TDOA/FDOA measurements in complex underwater environments. The algorithm initially makes use of the considerable capacity of a convolutional neural network (CNN) to extract profound spatial and frequency domain characteristics from multimodal data. These features are of paramount importance for the characterisation of underwater signal propagation, particularly in complex environments. Subsequently, through the use of a bidirectional gated recurrent unit (BiGRU), the algorithm is able to effectively capture long-term dependencies in time series data. This enables a more comprehensive analysis and understanding of the changing pattern of signals over time. Furthermore, the incorporation of an attention mechanism within the algorithm enables the model to focus more on the signal features that have a significant impact on localisation, while simultaneously suppressing the interference of extraneous information. This further enhances the efficiency of identifying and utilising the key signal features. ImTSWLS is employed to resolve the position and velocity data following the acquisition of the predicted TDOA/FDOA, thereby enabling the accurate estimation of the position and velocity of the mobile radiation source. The algorithm was subjected to a series of tests in a variety of simulated underwater environments, including different sea states, target motion speeds and base station configurations. The experimental results demonstrate that the algorithm exhibits a deviation of only 2.88 m/s in velocity estimation and 2.58 m in position estimation when the noise level is 20 dB. The algorithm presented in this paper demonstrates superior performance in both position and velocity estimation compared to other algorithms.

本研究的目的是解决复杂水下环境中 TDOA/FDOA 测量精度的问题，该问题受到水下环境的挑战性性质引起的多径效应和水声速变化的影响。为此，开发了一种新颖的协作定位算法，将注意力机制和卷积神经网络双向门控循环单元（CNN-BiGRU）与TDOA/FDOA和两步加权最小二乘法（ImTSWLS）相结合。该算法旨在提高复杂水下环境中TDOA/FDOA测量的精度。该算法最初利用卷积神经网络（CNN）的强大能力从多模态数据中提取深刻的空间和频域特征。这些特征对于水下信号传播的表征至关重要，特别是在复杂的环境中。随后，通过使用双向门控循环单元（BiGRU），该算法能够有效捕获时间序列数据中的长期依赖性。这使得能够更全面地分析和理解信号随时间变化的模式。此外，在算法中加入注意力机制使模型能够更多地关注对定位有重大影响的信号特征，同时抑制无关信息的干扰。这进一步提高了识别和利用关键信号特征的效率。 ImTSWLS 用于在获取预测的 TDOA/FDOA 后解析位置和速度数据，从而能够准确估计移动辐射源的位置和速度。 该算法在各种模拟水下环境中进行了一系列测试，包括不同的海况、目标运动速度和基站配置。实验结果表明，当噪声水平为20 dB时，该算法的速度估计偏差仅为2.88 m/s，位置估计偏差仅为2.58 m。与其他算法相比，本文提出的算法在位置和速度估计方面表现出优越的性能。