基于动作捕捉的无人机运动状态识别

无人机运动状态识别是无人机运行状态分析的基础,是实现无人机航迹预测的必要条件。对于非合作目标来说,动作捕捉系统可以有效采集其航迹数据。提出一种基于动作捕捉的无人机运动状态识别方法。首先,通过插值、重采样、滤波等方法对包含噪声的无人机航迹数据进行预处理;然后,通过特征提取与特征选择方法,针对速度、加速度、曲率、转角这4个运动参数,提取无人机运动特征;并分割无人机航迹。最后运用支持向量机的方法进行无人机运动状态识别,对速度、加速度、曲率的分类精度分别达到了95%、90%和100%。证明了本方法的可行性。

Motion Identification of UAV Based on Motion Capture

The UAV motion status recognition is the basis of UAV operation status analysis and it is a necessary condition for UAV track prediction. For non-cooperative targets, the motion capture system can effectively collect its track data. This paper proposes a UAV motion state recognition method based on motion capture. Firstly, the noise-containing drone track data is preprocessed by interpolation, resampling, filtering and other methods. Then, feature extraction and feature selection methods are used to extract the four motion parameters: velocity, acceleration, curvature, and rotation angle. Drone movement characteristics and segmented drone tracks. Finally, using the method of support vector machine to identify the motion state of UAV, the classification accuracy of speed, acceleration and curvature reached 95%, 90% and 100% respectively. The feasibility of this method is proved.