2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT)最新文献

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Predicting show rates in air cargo transport 预测航空货物运输的显示率

2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT) Pub Date : 2020-02-01 DOI: 10.1109/AIDA-AT48540.2020.9049209

A. Brieden, P. Gritzmann

引用次数: 5

Data-driven Conflict Detection Enhancement in 3D Airspace with Machine Learning 基于机器学习的三维空域数据驱动冲突检测增强

2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT) Pub Date : 2020-02-01 DOI: 10.1109/AIDA-AT48540.2020.9049180

Zhengyi Wang, M. Liang, D. Delahaye

{"title":"Data-driven Conflict Detection Enhancement in 3D Airspace with Machine Learning","authors":"Zhengyi Wang, M. Liang, D. Delahaye","doi":"10.1109/AIDA-AT48540.2020.9049180","DOIUrl":"https://doi.org/10.1109/AIDA-AT48540.2020.9049180","url":null,"abstract":"Trajectory prediction with Closest Point of Approach (CPA) concept is a fundamental element of aircraft Conflict Detection (CD) problem. Conventional motion-based CPA prediction model generally assumes that aircraft is flying in straight line with constant speed. But due to environment uncertainties and ground speed changes, this conventional method frequently lacks accuracy in the real world with a high rate of false alarms and missed detections. In this paper, we introduce a novel automated data-driven CD framework with Machine Learning (ML) for 3D CPA prediction in a lookahead time of less than 20 minutes. Firstly, a 3D CPA model with cylindrical norm is proposed as the baseline. Then, data preparation with Mode-S observation data in France is explained, including data collection and data processing, to convert raw Mode-S data to the close-to-reality dataset. Furthermore, feature engineering is applied to build up a feature set with 16 features. Finally, four prevailing ML models are used to predict the time, horizontal distance and vertical distance of CPA in 3D airspace. CD is conducted based on the predicted values. The prediction and CD results show that all proposed ML models outperform the baseline model. Especially, GBM and FFNNs could strongly enhance the performance of CD.","PeriodicalId":106277,"journal":{"name":"2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT)","volume":"51 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123461494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Convolutional Neural Network for Multipath Detection in GNSS Receivers 基于卷积神经网络的GNSS接收机多路径检测

2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT) Pub Date : 2019-11-06 DOI: 10.1109/AIDA-AT48540.2020.9049188

Evgenii Munin, Antoine Blais, Nicolas P. Couellan

{"title":"Convolutional Neural Network for Multipath Detection in GNSS Receivers","authors":"Evgenii Munin, Antoine Blais, Nicolas P. Couellan","doi":"10.1109/AIDA-AT48540.2020.9049188","DOIUrl":"https://doi.org/10.1109/AIDA-AT48540.2020.9049188","url":null,"abstract":"Global Navigation Satellite System (GNSS) signals are subject to different kinds of events causing significant errors in positioning. This work explores the application of Machine Learning (ML) methods of anomaly detection applied to GNSS receiver signals. More specifically, our study focuses on multipath contamination, using samples of the correlator output signal. The GPS L1 C/A signal data is used and sourced directly from the correlator output. To extract the important features and patterns from such data, we use deep convolutional neural networks (CNN), which have proven to be efficient in image analysis in particular. To take advantage of CNN, the correlator output signal is mapped as a 2D input image and fed to the convolutional layers of a neural network. The network automatically extracts the relevant features from the input samples and proceeds with the multipath detection. We train the CNN using synthetic signals. To optimize the model architecture with respect to the GNSS correlator complexity, the evaluation of the CNN performance is done as a function of the number of correlator output points.","PeriodicalId":106277,"journal":{"name":"2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT)","volume":"57 12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130411363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

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