WITHDRAWN: Simulation-Based Evaluation of GNSS–IMU and LiDAR Sensor Fusion for UAV Tracking Using Extended Kalman Filters

This study presents a simulation-based comparison of GNSS–IMU fusion and LiDAR-based state estimation for cooperative UAV leader-follower tracking using Extended Kalman Filters (EKFs) in the AirSim environment. Two distinct pipelines are developed: one integrating GNSS and IMU measurements, and another using LiDAR point cloud clustering via DBSCAN for relative position tracking. Both systems account for realistic sensor noise, communication delay, and varied update rates. We evaluate the performance of our filters using the accuracy of the states, filter stability, and covariance evolution under multiple noise models, including Gaussian and heavy-tailed distributions. Experimental results show the trade-offs between absolute (GNSS-based) and relative (LiDAR-based) sensing modalities, offering insights into robust state estimation strategies for multi-agent UAV operations in imperfect conditions.