Adaptive Tuning of the Unscented Kalman Filter using Particle Swarm Optimization for Inertial-GPS Sensor Fusion Systems
Abstract
Accurate vehicle positioning requires effective IMU-GPS fusion, yet prior methods-EKF, UKF, ML, GA, and DE-suffer from nonlinearity, instability, or high computational cost. This study introduces a PSO-based adaptive tuning framework for optimizing UKF parameters ({\alpha}, \b{eta}, \k{appa}, Q, R), evaluated in CARLA 0.9.14 using a Tesla Model 3 under diverse maneuvers and environmental conditions. Within defined parameter bounds, convergence stabilized within 15 generations, achieving an 82.14% accuracy improvement over manual tuning and reducing IMU drift by up to 21,606.59m. Multi-trial statistical validation confirmed consistent gains with low confidence intervals. With update times remaining below the 10 ms real-time threshold, the PSO-tuned UKF demonstrates practical localization performance for dynamic, GPS-challenged conditions.
Keywords
Cite
@article{arxiv.2601.01578,
title = {Adaptive Tuning of the Unscented Kalman Filter using Particle Swarm Optimization for Inertial-GPS Sensor Fusion Systems},
author = {Psyche T. Malabo and Bobby D. Gerardo},
journal= {arXiv preprint arXiv:2601.01578},
year = {2026}
}