在GPS拒止环境中使用基于地标的定位进行安全导航
Secure Navigation using Landmark-based Localization in a GPS-denied Environment
AI总结：
本文提出将基于地标的定位（LanBLoc）与扩展卡尔曼滤波器（EKF）结合，利用安全轨迹和凸包点包含测试实现GPS拒止环境下的安全导航，实验显示纵向误差为6.51%、ADE为2.97米、FDE为3.27米。
摘要：
在现代战场场景中，导航对GPS的依赖可能成为一个关键脆弱点。对手经常采用战术来拒止或欺骗GPS信号，因此需要替代方法来实现移动部队的定位与导航。诸如DV-HOP等免测距定位方法依赖基于无线电的锚节点及其平均跳距，在动态且稀疏的网络拓扑中其精度和稳定性较差。基于视觉的方法如SLAM和视觉里程计使用传感器融合技术进行地图生成和位姿估计，但这些方法更复杂且计算开销更大。本文提出了一种新颖的框架，将基于地标的定位（LanBLoc）与扩展卡尔曼滤波器（EKF）相结合，以预测战场中移动实体的未来状态。我们的框架利用部队控制中心生成的安全轨迹信息，该信息考虑了可识别地标和预定义的危险地图。该框架对轨迹段的凸包执行点包含测试，以确保移动实体的安全性和生存能力，并确定下一步前进决策。我们针对两种不同方法（使用EKF和不使用EKF）呈现了一个模拟战场场景，引导移动实体通过无障碍且无危险的路径。使用所提出的方法，我们观察到安全轨迹估计的纵向百分比误差为6.51%，平均位移误差（ADE）为2.97米，最终位移误差（FDE）为3.27米。结果表明，我们的方法不仅通过将移动单元保持在安全轨迹内来确保其安全，还通过适应不断演变的威胁态势来提高作战效能。
英文摘要：
In modern battlefield scenarios, the reliance on GPS for navigation can be a critical vulnerability. Adversaries often employ tactics to deny or deceive GPS signals, necessitating alternative methods for the localization and navigation of mobile troops. Range-free localization methods such as DV-HOP rely on radio-based anchors and their average hop distance which suffers from accuracy and stability in a dynamic and sparse network topology. Vision-based approaches like SLAM and Visual Odometry use sensor fusion techniques for map generation and pose estimation that are more sophisticated and computationally expensive. This paper proposes a novel framework that integrates landmark-based localization (LanBLoc) with an Extended Kalman Filter (EKF) to predict the future state of moving entities along the battlefield. Our framework utilizes safe trajectory information generated by the troop control center by considering identifiable landmarks and pre-defined hazard maps. It performs point inclusion tests on the convex hull of the trajectory segments to ensure the safety and survivability of a moving entity and determines the next point forward decisions. We present a simulated battlefield scenario for two different approaches (with EKF and without EKF) that guide a moving entity through an obstacle and hazard-free path. Using the proposed method, we observed a percent error of 6.51% lengthwise in safe trajectory estimation with an Average Displacement Error (ADE) of 2.97m and a Final Displacement Error (FDE) of 3.27m. The results demonstrate that our approach not only ensures the safety of the mobile units by keeping them within the secure trajectory but also enhances operational effectiveness by adapting to the evolving threat landscape.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
