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Ionospheric delay prediction and code-carrier divergence testing for GBAS using neural network and GPS L1

机译：基于神经网络和GPS L1的GBAS的电离层延迟预测和码载偏差测试

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This paper presents a Neural Network (NN) model for predicting Ionospheric Delay (ID) which is used particularly in the Ground Based Augmentation System (GBAS) as a new and alternative approach. It is vital to have positioning solutions exhibiting high levels of performance in terms of navigation for aircraft to counter systematic errors in broadcast correction ranging measurements associated with GBAS, such as ID when using a Global Positioning System (GPS) L1 frequency receiver. In principle, ID can be simply estimated with the aid of dual frequency receivers (GPS L1 and L2) or a new GPS signal (L5). However, the GBAS relies only on the L1 frequency as the L2 frequency is not protected by Aeronautical Radio Navigation Service (ARNS) and L5 is not fully functional. In this context, the NN model is proposed to predict the ID from only GPS L1 pseudorange measurements. Benchmarking performed between prediction and conventional dual frequencies (GPS L1 and L2) illustrates the validity of the proposed method. In addition, divergence tests are performed to assess the effectiveness of predicted ID on the code-carrier. The possibility that ID type systematic and temporal errors in GBAS ranging measurements can be predicted accurately with only GPS L1 measurements is investigated. NN can also be used in the GBAS to reduce the code-carrier divergence effects between ground and airborne users. (C) 2017 Elsevier Masson SAS. All rights reserved.
机译：本文提出了一种用于预测电离层延迟（ID）的神经网络（NN）模型，该模型特别用于基于地面的增强系统（GBAS）中，作为一种新的替代方法。至关重要的是，对于飞机而言，拥有在导航方面表现出高水平性能的定位解决方案，以应对与GBAS相关的广播校正测距测量中的系统错误，例如使用全球定位系统（GPS）L1频率接收器时的ID。原则上，可以借助双频接收机（GPS L1和L2）或新的GPS信号（L5）简单地估算ID。但是，GBAS仅依靠L1频率，因为L2频率不受航空无线电导航服务（ARNS）的保护，并且L5不能完全正常工作。在这种情况下，提出了NN模型以仅从GPS L1伪距测量值预测ID。在预测和常规双频（GPS L1和L2）之间执行的基准测试说明了该方法的有效性。另外，执行散度测试以评估代码载体上预测ID的有效性。研究了仅使用GPS L1测量就可以准确预测GBAS测距中的ID类型系统和时间误差的可能性。 NN也可以在GBAS中使用，以减少地面用户与机载用户之间的码载波差异效应。 （C）2017 Elsevier Masson SAS。版权所有。

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