航空科学技术2025，Vol.36Issue(5)：89-96,8.DOI:10.19452/j.issn1007-5453.2025.05.011
基于改进Sine模型的轻型无人机跟踪算法
Light UAV Tracking Algorithm Based on Improved Sine Model
摘要
Abstract
How to accurately track incoming enemy drones on the battlefield is the key to accurately strike them.In view of the problem that when light drones fly horizontally at a uniform speed,their flight trajectory floats and jitters due to their small size,light weight and unstable flight control system,making them difficult to track,this paper proposes a modified unbiased measurement conversion Kalman filter(MUCMKF)tracking algorithm based on the improved sine model.In view of the problem that the traditional Sine model requires the acceleration variance and maneuvering frequency to be set in advance,which may cause the tracking system to diverge,this algorithm proposes to use the one-step prediction value of the target's current position and the current position estimate to adaptively estimate the acceleration variance,and associate the maneuvering frequency with the acceleration variance to achieve simultaneous adaptive adjustment of the acceleration variance and maneuvering frequency.On this basis,the modified unbiased measurement conversion Kalman filter algorithm is used to filter and track the real flight trajectory of the light drone.The simulation results show that,compared with the uniform velocity model(CV),uniform acceleration model(CA),the current statistical model CS and the conventional Sine model,the tracking algorithm based on the improved Sine model can better adapt to the flight characteristics of light UAVs,with the accuracy improved by 44%,59%,42%and 37%respectively,and has good engineering application prospects.关键词
轻型无人机/目标跟踪/Sine模型/MUCMKF算法/参数自适应Key words
light UAV/target tracking/Sine model/MUCMKF algorithm/parameter adaptation分类
航空航天引用本文复制引用
陈伟,李星秀,吴盘龙,何山,赵保琛..基于改进Sine模型的轻型无人机跟踪算法[J].航空科学技术,2025,36(5):89-96,8.基金项目
航空科学基金(2022Z037059001,20220001059001) （2022Z037059001,20220001059001）
江苏省卓越博士后计划(JB23147) （JB23147）
江苏省自然科学基金(BK20241463) Aeronautical Science Foundation of China(2022Z037059001,20220001059001) （BK20241463）
Jiangsu Funding Program for Excellent Postdoctoral Talent(JB23147) （JB23147）
Natural Science Foundation of Jiangsu Province(BK20241463) （BK20241463）
