{"title":"多目标、多传感器舰船跟踪与分类","authors":"Leonard Kosta, John Irvine, Laura Seaman, H. Xi","doi":"10.1109/HPEC.2019.8916332","DOIUrl":null,"url":null,"abstract":"Government agencies such as DARPA wish to know the numbers, locations, tracks, and types of vessels moving through strategically important regions of the ocean. We implement a multiple hypothesis testing algorithm to simultaneously track dozens of ships with longitude and latitude data from many sensors, then use a combination of behavioral fingerprinting and deep learning techniques to classify each vessel by type. The number of targets is unknown a priori. We achieve both high track purity and high classification accuracy on several datasets.","PeriodicalId":184253,"journal":{"name":"2019 IEEE High Performance Extreme Computing Conference (HPEC)","volume":"126 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Many-target, Many-sensor Ship Tracking and Classification\",\"authors\":\"Leonard Kosta, John Irvine, Laura Seaman, H. Xi\",\"doi\":\"10.1109/HPEC.2019.8916332\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Government agencies such as DARPA wish to know the numbers, locations, tracks, and types of vessels moving through strategically important regions of the ocean. We implement a multiple hypothesis testing algorithm to simultaneously track dozens of ships with longitude and latitude data from many sensors, then use a combination of behavioral fingerprinting and deep learning techniques to classify each vessel by type. The number of targets is unknown a priori. We achieve both high track purity and high classification accuracy on several datasets.\",\"PeriodicalId\":184253,\"journal\":{\"name\":\"2019 IEEE High Performance Extreme Computing Conference (HPEC)\",\"volume\":\"126 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE High Performance Extreme Computing Conference (HPEC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/HPEC.2019.8916332\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE High Performance Extreme Computing Conference (HPEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HPEC.2019.8916332","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Many-target, Many-sensor Ship Tracking and Classification
Government agencies such as DARPA wish to know the numbers, locations, tracks, and types of vessels moving through strategically important regions of the ocean. We implement a multiple hypothesis testing algorithm to simultaneously track dozens of ships with longitude and latitude data from many sensors, then use a combination of behavioral fingerprinting and deep learning techniques to classify each vessel by type. The number of targets is unknown a priori. We achieve both high track purity and high classification accuracy on several datasets.