基于激光雷达的无人机违规航行轨迹数据自动捕获方法

为了解决传统方法对无人机违规航行轨迹数据捕获的精度和实时性低的问题,通过激光雷达研究了一种新的无人机违规航行轨迹数据自动捕获方法。建立激光雷达信号和目标场景作用过程模型,将每束激光在目标场景表面的映射部分当成激光脚印,完成对激光束相应激光脚印的响应函数和激光雷达发射信号的时间分布函数的卷积计算,获取激光脚印相应目标区域和激光雷达信号作用后反馈的回波信号。依据反馈的回波信号建立单次成像回波峰值点轨迹分布模型,获取无人机航行轨迹数据。针对无人机实际航行轨迹和预定义航行轨迹,采用优化的修正豪斯多夫距离公式(MHD)进行轨迹数据匹配程度衡量,按照经验设定阈值,若匹配度超过阈值,则认为实际轨迹与预设定无人机航行轨迹不匹配,将相应实际轨迹当成无人机违规航行轨迹,对违规航行轨迹数据进行捕获。结果表明:所提方法可及时检测处无人机违规航迹,报警时间比其他方法时效性高;所提方法捕获的无人机违规航行轨迹数据与真实数据偏差小。可见所提方法实时性与精度均较高。

Automatic Acquisition Method of Unmanned Aerial Vehicle Trajectory Data Based on Lidar

In order to solve the problem of low accuracy and real-time performance of traditional methods for UAV irregularity trajectory data acquisition, a new automatic method for UAV irregularity trajectory data acquisition was studied by means of lidar. The model of action process of lidar signal and target scene was established. The mapping part of each laser beam on the surface of target scene was regarded as a laser footprint. The convolution calculation of the response function of the corresponding laser footprint and the time distribution function of the lidar transmitting signal was completed. The corresponding target area of the laser footprint and the echo signal of the feedback of the lidar signal were obtained. Based on the feedback echo signal, the peak trajectory distribution model of single imaging echo was established to obtain the UAV trajectory data. Aiming at the actual trajectory and predefined trajectory of UAV, the optimized distance formula MHD was used to measure the degree of trajectory data matching. The threshold value was set according to experience. If the value of matching degree exceeds the threshold value, the actual trajectory does not match the predefined trajectory of UAV. The corresponding actual trajectory was regarded as the irregular trajectory of UAV, and the irregular trajectory data is captured. The results show that the proposed method can detect the unmanned aerial vehicle (UAV) irregularities in time, and the alarm time is more time-consuming than other methods. The errors between the trajectory data captured by the proposed method and the real data are smaller. It can be seen that the proposed method has high real-time performance and accuracy.