The autonomous underwater vehicle(AUV)-aided localization techniques for underwater acoustic devices show promising applications in many scenarios, and most researches in this area are based on the time of arrival (ToA) or the time difference of arrival (TDoA) measurements. However, these measurements are not readily available. To develop a more universally applicable scheme, we investigate the possibility of employing the Doppler shift measurements for underwater localization of acoustic devices in this paper. To be specific, we employ a low-complexity algorithm for Doppler estimation, and prove that the estimation error can be well approximated by zero-mean Gaussian distribution. Based on the Doppler estimates, we can obtain a series of nonlinear equations. To solve them, we propose a two-phase linear algorithm to obtain high-accuracy position information of the target devices. Compared with the conventional iterative algorithms, the proposed one does not require initial estimate. Both the closed-form localization error and the Cramér-Rao lower bound are presented. They prove to be consistent for reasonably small Doppler estimation error. Besides, we conduct simulations to verify the theoretical analysis. Moreover, the complexity of the proposed algorithm only grows linearly with the number of Doppler shift measurements.

中文翻译：

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基于多普勒频移测量的水下声学设备 AUV 辅助定位

用于水声设备的自主水下航行器（AUV）辅助定位技术在许多场景中显示出有前景的应用，并且该领域的大多数研究基于到达时间（ToA）或到达时间差（TDoA）测量。然而，这些测量并不容易获得。为了开发更普遍适用的方案，我们研究了在本文中采用多普勒频移测量进行声学设备水下定位的可能性。具体而言，我们采用低复杂度算法进行多普勒估计，并证明估计误差可以用零均值高斯分布很好地近似。基于多普勒估计，我们可以得到一系列非线性方程。为了解决它们，我们提出了一种两阶段线性算法来获得目标设备的高精度位置信息。与传统的迭代算法相比，所提出的算法不需要初始估计。封闭形式的定位误差和 Cramér-Rao 下界都被呈现出来。对于相当小的多普勒估计误差，它们被证明是一致的。此外，我们进行了仿真以验证理论分析。此外，所提出算法的复杂度仅随着多普勒频移测量的数量线性增长。此外，我们进行了仿真以验证理论分析。此外，所提出算法的复杂度仅随着多普勒频移测量的数量线性增长。此外，我们进行了仿真以验证理论分析。此外，所提出算法的复杂度仅随着多普勒频移测量的数量线性增长。