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Secure Navigation using Landmark-based Localization in a GPS-denied Environment

Ganesh Sapkota, Sanjay Madria

TL;DR

This work addresses secure, GPS-denied battlefield navigation by fusing landmark-based localization (LanBLoc) with an adapted Extended Kalman Filter (EKF) under a battlefield motion model. Landmark anchors are identified via YOLOv8 and distance estimates are obtained through stereo matching, enabling precise trilateration with RMSEs on the order of $0.0142$ m in $x$ and $0.039$ m in $y$. The trajectory predictor integrates the nonlinear motion model with EKF updates from LanBLoc measurements and enforces safety via convex-hull checks on trajectory segments, ensuring movement within hazard-free regions. Experiments on MSTlandmark datasets show that incorporating EKF reduces percent error from $10.49\%$ to $6.51\%$, and lowers ADE/FDE from $5.56/5.99$ m to $2.97/3.27$ m, demonstrating improved localization accuracy and safer operational paths. Overall, the proposed framework enhances robustness and responsiveness of secure navigation in contested environments, enabling real-time, adaptive maneuvering without reliance on GPS signals.

Abstract

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.

Secure Navigation using Landmark-based Localization in a GPS-denied Environment

TL;DR

This work addresses secure, GPS-denied battlefield navigation by fusing landmark-based localization (LanBLoc) with an adapted Extended Kalman Filter (EKF) under a battlefield motion model. Landmark anchors are identified via YOLOv8 and distance estimates are obtained through stereo matching, enabling precise trilateration with RMSEs on the order of m in and m in . The trajectory predictor integrates the nonlinear motion model with EKF updates from LanBLoc measurements and enforces safety via convex-hull checks on trajectory segments, ensuring movement within hazard-free regions. Experiments on MSTlandmark datasets show that incorporating EKF reduces percent error from to , and lowers ADE/FDE from m to m, demonstrating improved localization accuracy and safer operational paths. Overall, the proposed framework enhances robustness and responsiveness of secure navigation in contested environments, enabling real-time, adaptive maneuvering without reliance on GPS signals.

Abstract

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.
Paper Structure (22 sections, 17 equations, 12 figures, 2 tables, 4 algorithms)

This paper contains 22 sections, 17 equations, 12 figures, 2 tables, 4 algorithms.

Table of Contents

  1. Introduction
  2. Background and Related Works
  3. Our Methodology and System Model
  4. System Overview
  5. Landmark-based moving object localization (LanBLoc)
  6. Motion Model of Mobile Entity in the Battlefield
  7. Velocity Update:
  8. Heading Update:
  9. Position Update:
  10. Trajectory Prediction using Extended Kalman Filter based battlefield motion model
  11. State and Measurement Models
  12. Prediction Step
  13. Update Step
  14. Safety Check using Convex Hull and Point in Polygon Method
  15. Experiments and Results
  16. ...and 7 more sections

Figures (12)

  • Figure 1: Showing an example Safe Path in the Battlefield Map containing landmarks, obstacles, hazards components
  • Figure 2: Safe trajectory as a series of path segments
  • Figure 3: Extended Kalman Filter adapted based on battlefield motion model
  • Figure 4: System Overview of LanBloc framework. Distance estimation and landmark recognition steps and the results after two operations are fused to calculate the position of an unknown node.
  • Figure 5: Stereo Camera Setup for MSTlandmarkStereov1 Collection
  • ...and 7 more figures