{"title":"使用对流层传播预测和机器学习预测AIS接收","authors":"Z. Vanche, A. Renaud, A. Napoli","doi":"10.23919/USNC-URSI52669.2022.9887465","DOIUrl":null,"url":null,"abstract":"The aim of this paper is to present a methodology for modelling and predicting the coverage of an Automatic Identification System (AIS) station based on tropospheric index forecast maps and modelling methods from machine learning. The aim of this work is to cartographically represent the areas in which the AIS signals emitted by ships will be received by a coastal station. This work contributes to the improvement of maritime situational awareness and to the detection of anomalies at sea [1], and in particular to the identification of AIS message falsifications [2] (ubiquity of a vessel by identity theft, falsification of GPS positions and deactivation of AIS).","PeriodicalId":104242,"journal":{"name":"2022 IEEE USNC-URSI Radio Science Meeting (Joint with AP-S Symposium)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Predicting AIS reception using tropospheric propagation forecast and machine learning\",\"authors\":\"Z. Vanche, A. Renaud, A. Napoli\",\"doi\":\"10.23919/USNC-URSI52669.2022.9887465\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The aim of this paper is to present a methodology for modelling and predicting the coverage of an Automatic Identification System (AIS) station based on tropospheric index forecast maps and modelling methods from machine learning. The aim of this work is to cartographically represent the areas in which the AIS signals emitted by ships will be received by a coastal station. This work contributes to the improvement of maritime situational awareness and to the detection of anomalies at sea [1], and in particular to the identification of AIS message falsifications [2] (ubiquity of a vessel by identity theft, falsification of GPS positions and deactivation of AIS).\",\"PeriodicalId\":104242,\"journal\":{\"name\":\"2022 IEEE USNC-URSI Radio Science Meeting (Joint with AP-S Symposium)\",\"volume\":\"101 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-07-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE USNC-URSI Radio Science Meeting (Joint with AP-S Symposium)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/USNC-URSI52669.2022.9887465\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE USNC-URSI Radio Science Meeting (Joint with AP-S Symposium)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/USNC-URSI52669.2022.9887465","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Predicting AIS reception using tropospheric propagation forecast and machine learning
The aim of this paper is to present a methodology for modelling and predicting the coverage of an Automatic Identification System (AIS) station based on tropospheric index forecast maps and modelling methods from machine learning. The aim of this work is to cartographically represent the areas in which the AIS signals emitted by ships will be received by a coastal station. This work contributes to the improvement of maritime situational awareness and to the detection of anomalies at sea [1], and in particular to the identification of AIS message falsifications [2] (ubiquity of a vessel by identity theft, falsification of GPS positions and deactivation of AIS).