Jian Zhou, Yu Su, Yuhe Qiu, Xiaoyou He, and Zhihong Rao
PSO-KF, virtual measurement, prediction.
The traditional Kalman filter (KF) algorithm solely relies on the historical positioning information of the unmanned aerial vehicle (UAV) itself for UAV position prediction. However, it fails to accurately estimate the position after a longer period of time. To address this issue, this paper proposes an improved PSO- KF algorithm. The algorithm utilises a priori route information to generate virtual measurement data and performs real-time state corrections, thereby enhancing the accuracy of position estimation. Additionally, to overcome the challenge of acquiring statistical characteristics of measurement data noise, this paper introduces a particle swarm optimisation algorithm to enhance and optimise the measurement noise covariance matrix. Mathematical simulation results verify that the proposed algorithm outperforms the traditional KF in UAV position prediction, particularly in longer time prediction scenarios, with significant improvements observed.
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