摘要
为解决传统雷达探测设备面对“低小慢”无人机时产生的难检测与易突防问题,通过深度卷积神经网络对空中无人机进行实时识别,提取目标的类别与像空间位置信息;根据无人机像空间位置在时域下的变化趋势,绘制无人机飞行映射轨迹;利用长短期记忆网络对飞行映射轨迹进行预测,获取无人机在未来时域内的预测航迹方向,实现对无人机的预警跟踪、实时检测与轨迹推断。结果表明,所提出的算法中目标识别平均准确率可达到82%,轨迹预测平均准确率可达到80%计算速度可达到24帧/秒,可见能够在地基计算平台下对空中无人机进行实时精确预警,可以有效地防止识别领空内的非合作无人机渗透与突防。
Aiming at the problems of difficult detection and easy penetration of traditional radar detection equipment when facing low-speed-small unmanned aerial vehicle(UAV),an drone recognition and trajectory prediction algorithm based on deep convolution neural network and long-short-term memory neural network is proposed.Deep convolution neural network is used to identify the drone in real time and the target category and image space position information were extracted.According to the change trend of UAV,image space position in time domain,the drone flight mapping trajectory can be drawn.The long-short-term memory network is used to predict the flight mapping trajectory,and the predicted track direction of UAV in the future time domain is obtained to realize the unmanned aerial vehicle early warning and tracking,real-time detection and trajectory inference.The experimental results show that the average accuracy of target recognition and trajectory prediction can reach 82%and 80%in 24 frames per second.It can give real-time and accurate warning to UAV in the ground-based computing platform,and can effectively prevent the penetration and penetration of non-cooperative UAV in the airspace.
作者
孙颢洋
王欣
曹昭睿
白帆
王兴
郝永平
王俊杰
SUN Hao-yang;WANG Xin;CAO Zhao-rui;BAI Fan;WANG Xing;HAO Yong-ping;WANG Jun-jie(School of Equipment Engineering, Shenyang Ligong University, Shenyang 110159, China;School of Mechanical Engineering,Shenyang Ligong University, Shenyang 110159, China;School of Science, Shenyang Ligong University, Shenyang 110159, China)
出处
《科学技术与工程》
北大核心
2021年第22期9461-9469,共9页
Science Technology and Engineering
基金
装备预研重点实验室基金(6142107190207)。
关键词
无人机预警
卷积神经网络
长短记忆网络
目标识别
轨迹预测
deep learning
convolution neural network
long short memory network
machine vision
vehicle behavior
