现代防御技术2025，Vol.53Issue(5)：215-226,12.DOI:10.3969/j.issn.1009-086x.2025.05.022
改进鸽群优化的随机森林无人机传感器故障预测技术
Fault Prediction Technology for UAVs Sensor Based on Random Forest Improved by Pigeon-Inspired Optimization
摘要
Abstract
The prediction of UAV sensor faults is of great significance for improving system reliability and safety.However,current prediction methods suffer from insufficient accuracy and high computational complexity.To address this issue,this study proposed a random forest fault prediction algorithm based on improved pigeon-inspired optimization(PIO).The wavelet packet transform(WPT)was employed to extract fault features,and the Levy flight mechanism was introduced to form a mutation mechanism to enhance population diversity,thereby alleviating the problem of PIO easily falling into local optima and enhancing both the convergence speed and global optimization ability of the algorithm.The improved PIO was then applied to the random forest algorithm to achieve autonomous hyperparameter optimization,enabling early prediction of sensor faults.Simulation results show that the random forest algorithm optimized by the improved PIO converged faster and achieved an accuracy improvement of more than 20%compared with traditional algorithms,indicating the effectiveness and superiority of the proposed method in UAV sensor fault prediction.关键词
小波包变换/改进鸽群算法/随机森林/莱维飞行/无人机传感器故障Key words
wavelet packet transform(WPT)/improved pigeon-inspired optimization(PIO)/random forest/Levy flight/drone sensor fault分类
信息技术与安全科学引用本文复制引用
刘媛媛,袁荣,邵书义,陈谋..改进鸽群优化的随机森林无人机传感器故障预测技术[J].现代防御技术,2025,53(5):215-226,12.基金项目
国家自然科学基金联合基金重点支持项目(U2013201) （U2013201）
