摘要
基于分布式多传感器航迹融合系统,采用小波神经网络(Wavelet Neural Networks,WNNs)理论,研究了自动相关监视(Automatic Dependent Surveillance,ADS)与多雷达信息的航迹融合问题。以位置方差权重因子和报告周期权重因子构建WNNs,并实时估计各航迹信息的可信度,融合输出综合航迹。仿真结果表明,该算法对航迹的融合结果优于互协方差融合等算法,并具有更强的鲁棒性,有效提高了监视能力的完整性。
Based on the distributed multi-sensor track fusion system,the problem of Automatic Dependent Surveillance（ADS） and multi-radar track fusion is studied with Wavelet Neural Networks（WNNs）.The local estimation errors and position reporting period are introduced to WNNs which calculate the sensor reliability in real time.Then the system uses the reliability to fuse and give out the synthetic track.Simulation results show that,this algorithm has an outstanding performance and robustness over BS algorithm,and improves the integrality of surveillance.
出处
《计算机工程与应用》
CSCD
北大核心
2011年第27期225-227,248,共4页
Computer Engineering and Applications
基金
国家空管科研基金资助(No.GKG200802005)
关键词
空中交通管制
航迹融合
小波神经网络
自动相关监视
可信度
air traffic control
track fusion
Wavelet Neural Networks（WNNs）
Automatic Dependent Surveillance（ADS）
reliability
