An improved IMM-KF for UAV position prediction

Yuhe Qiu; Yu Su; Xiaoyou He

doi:10.1117/12.3010570

1 December 2023 An improved IMM-KF for UAV position prediction

Yuhe Qiu, Yu Su, Xiaoyou He

Proceedings Volume 12940, Third International Conference on Control and Intelligent Robotics (ICCIR 2023); 1294003 (2023) https://doi.org/10.1117/12.3010570
Event: Third International Conference on Control and Intelligent Robotics (ICCIR 2023), 2023, Sipsongpanna, China

Abstract

Traditional Kalman filter algorithms used for predicting drone positions rely solely on the historical positioning information of the drone itself, which makes it challenging to accurately estimate the position of the drone over longer time periods. In this paper, an improved IMM-KF (Interacting Multiple Models-Kalman Filter) algorithm is proposed. This algorithm incorporates a priori information about the drone’s planned flight route and the limited states of the drone, such as position, velocity, and acceleration, to achieve real-time prediction of drone position information. The advantages of interacting multiple models are also leveraged in the algorithm. Mathematical simulation results validate that this improved algorithm outperforms traditional Kalman filters in predicting drone positions, with the improvement becoming more pronounced as the prediction time horizon increases.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yuhe Qiu, Yu Su, and Xiaoyou He "An improved IMM-KF for UAV position prediction", Proc. SPIE 12940, Third International Conference on Control and Intelligent Robotics (ICCIR 2023), 1294003 (1 December 2023); https://doi.org/10.1117/12.3010570

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