Terrain-aided navigation (TAN) is a promising technology for achieving accurate positioning of long-range autonomous underwater vehicles (AUVs). In TAN systems, particle filters (PFs) are widely used to estimate the AUV’s location. However, PFs suffer from inefficient particle resampling and the assumption of constant particle process noise, both of which reduce the AUV positioning accuracy. In this article, we propose an improved TAN method based on cubature PFs (CPFs). Specifically, we design an adaptive particle process noise that varies the particle search range according to positioning errors. Additionally, a CPF-based resampling mechanism is introduced, in which particles are forced into the surrounding region of high-weight particles, rather than a single point. Simulations performed on actual bathymetric maps demonstrate that our proposed CPF-based TAN can improve the average positioning accuracy by 4.2%, 9.4%, 12.7%, 15.8%, and 17.2%, respectively, compared with the TANs based on neural network PF (NNPF), auxiliary PF (APF), data-driven PF (DDPF), fuzzy PF (FPF), and PF, which verifies the effectiveness of our proposed method.

中文翻译：

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基于体积粒子滤波器的远程AUV地形辅助导航

地形辅助导航（TAN）是实现远程自主水下航行器（AUV）精确定位的一项有前景的技术。在 TAN 系统中，粒子滤波器 (PF) 广泛用于估计 AUV 的位置。然而，PF 受到低效的粒子重采样和恒定粒子过程噪声的假设的影响，这两者都会降低 AUV 的定位精度。在本文中，我们提出了一种基于体积PF（CPF）的改进TAN方法。具体来说，我们设计了一种自适应粒子过程噪声，它根据定位误差改变粒子搜索范围。此外，还引入了基于 CPF 的重采样机制，其中粒子被强制进入高权重粒子的周围区域，而不是单个点。在实际测深地图上进行的仿真表明，与基于神经网络 PF (NNPF) 的 TAN 相比，我们提出的基于 CPF 的 TAN 可以将平均定位精度分别提高 4.2%、9.4%、12.7%、15.8% 和 17.2%。 ）、辅助PF（APF）、数据驱动PF（DDPF）、模糊PF（FPF）和PF，验证了我们提出的方法的有效性。