Improving the Position Accuracy and Computational Efficiency of UAV Terrain Aided Navigation Using a Two-Stage Hybrid Fuzzy Particle Filtering Method

Terrain Aided Navigation (TAN) technology has become increasingly important due to its effectiveness in environments where Global Positioning System (GPS) is unavailable. In recent years, TAN systems have been extensively researched for both aerial and underwater navigation applications. However, many TAN systems that rely on recursive Unmanned Aerial Vehicle (UAV) position estimation methods, such as Extended Kalman Filters (EKF), often face challenges with divergence and instability, particularly in highly non-linear systems. To address these issues, this paper proposes and investigates a hybrid two-stage TAN positioning system for UAVs that utilizes Particle Filter. To enhance the system’s robustness against uncertainties caused by noise and to estimate additional system states, a Fuzzy Particle Filter (FPF) is employed in the first stage. This approach introduces a novel terrain composite feature that enables a fuzzy expert system to analyze terrain non-linearities and dynamically adjust the number of particles in real-time. This design allows the UAV to be efficiently localized in GPS-denied environments while also reducing the computational complexity of the particle filter in real-time applications. In the second stage, an Error State Kalman Filter (ESKF) is implemented to estimate the UAV’s altitude. The ESKF is chosen over the conventional EKF method because it is more suitable for non-linear systems. Simulation results demonstrate that the proposed fuzzy-based terrain composite method achieves high positional accuracy while reducing computational time and memory usage.