基于深度学习的SMR与ADS-B数据融合研究
Study on Data Fusion of SMR and ADS-B Based on Deep Learning
傅鹏伟 1程擎1
作者信息
- 1. 中国民用航空飞行学院,四川 618300
- 折叠
摘要
阐述针对机场场面上各类航空器的运动场景,提出一种利用SMR和ADS-B信息融合从而进行实时监视的方法.该方法为了解决不同观测源的数据信息采集数据量不均衡的问题,用训练后的深度学习算法进行融合,并与最常用的卡尔曼滤波融合进行误差对比.结果表明,在数据信息不均衡的条件下,该方法精度更优,可靠性更高,具有一定的工程应用价值.
Abstract
This paper describes a method of real-time monitoring by using SMR and ADS-B information fusion based on the movement scenes of various aircrafts on the airport scene.In order to solve the problem of unbalanced amount of data collected from different observation sources,this method uses a trained deep learning algorithm for fusion,and compares the error with the most commonly used Kalman filter fusion.The results show that under the condition of unbalanced data information,the method has better accuracy and higher reliability,and has certain engineering application value.
关键词
深度学习/数据融合/场面监视雷达/广播式自动相关监视Key words
deep learning/data fusion/surface surveillance radar/automatic dependent surveillance-broadcast引用本文复制引用
出版年
2024
NETL
NSTL
万方数据