Underwater navigation is a challenging topic in the field of underwater exploration. Accurate and effective underwater navigation is necessary in applications such as 3D seafloor topography scanning, cable laying, deep-sea resource survey and wreck salvage. However, underwater navigation becomes difficult due to the variability and complexity of underwater environment. In the deep-sea environment, the ultra-short baseline positioning system (USBL) is very unstable, and the flying abnormal points and signal loss often occur. To solve these problems, this paper proposes a two-step filtering scheme combined with doppler velocity log (DVL). The first step is to construct a rough Monte Carlo particle filter (MPF) model for the autonomous underwater vehicle (AUV) position, and then select candidate particles with the strategy of minimizing the local backward dead reckoning (DR) errors. In the second step, the results of the first step are further smoothed and optimized using the weighted extended Kalman filter (WEKF). Finally, the robustness and practicability of the proposed method are verified by the multiple data obtained from several sea trials in the South-West Indian Ocean. The results also show that the integrated navigation framework is superior to single navigation method and other traditional filtering methods. The best balance between accuracy and computational cost is achieved.

水下导航是水下勘探领域的一个具有挑战性的课题。在 3D 海底地形扫描、电缆铺设、深海资源调查和沉船打捞等应用中，准确有效的水下导航是必不可少的。然而，由于水下环境的多变性和复杂性，水下导航变得困难。在深海环境下，超短基线定位系统（USBL）非常不稳定，经常出现飞行异常点和信号丢失。针对这些问题，本文提出了一种结合多普勒速度测井（DVL）的两步滤波方案。第一步是为自主水下航行器（AUV）位置构建一个粗糙的蒙特卡罗粒子滤波器（MPF）模型，然后用最小化局部后向航位推算（DR）误差的策略选择候选粒子。第二步，使用加权扩展卡尔曼滤波器（WEKF）进一步平滑和优化第一步的结果。最后，通过在西南印度洋多次海上试验获得的多个数据验证了所提出方法的稳健性和实用性。结果还表明，组合导航框架优于单一导航方法和其他传统过滤方法。实现了准确性和计算成本之间的最佳平衡。通过在西南印度洋多次海上试验获得的多个数据验证了所提出方法的稳健性和实用性。结果还表明，组合导航框架优于单一导航方法和其他传统过滤方法。实现了准确性和计算成本之间的最佳平衡。通过在西南印度洋的多次海上试验中获得的多个数据验证了所提出方法的稳健性和实用性。结果还表明，组合导航框架优于单一导航方法和其他传统过滤方法。实现了准确性和计算成本之间的最佳平衡。