Yin Hao, Li Dongguang, Wang Yue, Hong Xiaotong
School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China.
School of Mechatronical Engineering, North University of China, Taiyuan 038507, China.
Sensors (Basel). 2022 Aug 3;22(15):5800. doi: 10.3390/s22155800.
In preparation for the battlefields of the future, using unmanned aerial vehicles (UAV) loaded with multisensors to track dynamic targets has become the research focus in recent years. According to the air combat tracking scenarios and traditional multisensor weighted fusion algorithms, this paper contains designs of a new data fusion method using a global Kalman filter and LSTM prediction measurement variance, which uses an adaptive truncation mechanism to determine the optimal weights. The method considers the temporal correlation of the measured data and introduces a detection mechanism for maneuvering of targets. Numerical simulation results show the accuracy of the algorithm can be improved about 66% by training 871 flight data. Based on a mature refitted civil wing UAV platform, the field experiments verified the data fusion method for tracking dynamic target is effective, stable, and has generalization ability.
为应对未来战场，利用搭载多传感器的无人机（UAV）跟踪动态目标已成为近年来的研究重点。根据空战跟踪场景和传统多传感器加权融合算法，本文设计了一种新的数据融合方法，该方法采用全局卡尔曼滤波器和长短期记忆网络（LSTM）预测测量方差，并使用自适应截断机制来确定最优权重。该方法考虑了测量数据的时间相关性，并引入了目标机动检测机制。数值模拟结果表明，通过训练871组飞行数据，算法精度可提高约66%。基于成熟的改装民用翼无人机平台，现场实验验证了该动态目标跟踪数据融合方法有效、稳定且具有泛化能力。
