Zhang Xiaohui, Bai Wenqi, Liu Jun, Yang Songnan, Shang Ting, Liu Haolin
School of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China.
Sensors (Basel). 2025 Jun 13;25(12):3699. doi: 10.3390/s25123699.
Geospatial navigation in GPS-denied environments presents significant challenges, particularly for autonomous vehicles operating in complex, unmapped regions. We explore the Earth's geomagnetic field, a globally distributed and naturally occurring resource, as a reliable alternative for navigation. Since vehicles can only observe the geomagnetic field along their traversed paths, they must rely on incomplete information to infer the navigation strategy; therefore, we formulate the navigation problem as a partially observed Markov decision process (POMDP). To address this POMDP, we employ proximal policy optimization with long short-term memory (PPO-LSTM), a deep reinforcement learning framework that captures temporal dependencies and mitigates the effects of noise. Using real-world geomagnetic data from the international geomagnetic reference field (IGRF) model, we validate our approach through experiments under noisy conditions. The results demonstrate that PPO-LSTM outperforms baseline algorithms, achieving smoother trajectories and higher heading accuracy. This framework effectively handles the uncertainty and partial observability inherent in geomagnetic navigation, enabling robust policies that adapt to complex gradients and offering a robust solution for geospatial navigation.
在没有全球定位系统（GPS）的环境中进行地理空间导航面临重大挑战，特别是对于在复杂、未测绘区域运行的自动驾驶车辆而言。我们探索地球的地磁场，这是一种全球分布且自然存在的资源，作为一种可靠的导航替代方案。由于车辆只能沿着其行驶路径观测地磁场，它们必须依靠不完整的信息来推断导航策略；因此，我们将导航问题表述为一个部分可观测马尔可夫决策过程（POMDP）。为了解决这个POMDP，我们采用带有长短期记忆的近端策略优化（PPO-LSTM），这是一个深度强化学习框架，能够捕捉时间依赖性并减轻噪声的影响。利用来自国际地磁参考场（IGRF）模型的真实世界地磁数据，我们在有噪声的条件下通过实验验证了我们的方法。结果表明，PPO-LSTM优于基线算法，实现了更平滑的轨迹和更高的航向精度。该框架有效地处理了地磁导航中固有的不确定性和部分可观测性，能够制定适应复杂梯度的稳健策略，并为地理空间导航提供了一个稳健的解决方案。
