一种基于ADS-B的雷达误差实时融合校正算法

ADS-B-Based Algorithm for Real-Time Optimal Estimation of Radar Biases

针对我国中西部地区雷达未实现多重覆盖,大部分飞机未配备广播式自动相关监视(automatic dependentsurveillance-broadcast,ADS-B)机载设备,监视精度不高的问题,结合雷达误差模型理论,提出了基于ADS-B数据的雷达误差实时校正算法.该算法对同一时刻获得的多组ADS-B数据与雷达数据的差值进行数据融合,得到该时刻雷达误差的最优估计值,并对雷达进行误差校正.算例仿真结果表明:经该算法校正后,雷达测量的距离与真实距离之间的均方根误差约为50 m,测量的角度与真实角度之间的均方根误差约为0.04°,且校正后的航迹和真实飞行航迹基本重合.

In order to improve the surveillance accuracy of radars in China＇s central and western regions, where multiple coverage does not come true and majority airplanes are not equipped with ADS- B （automatic dependent surveillance-broadcast ）, an ADS-B-based algorithm for real-time optimal estimation of radar biases was proposed on the basis of the radar biases theory. The algorithm can achieve a real-time optimal estimation of radar biases by the data fusion based on the difference between ADS-B data and radar data at the same time as well as the real-time calibration of the radar. The simulation of a case study shows that the root mean square of biases between calibrated and real range values is about 50 m, the root mean square of biases between calibrated and real angle values is about 0.04°, and the calibrated radar trajectories are essentially coincident with actual trajectories.