{"title":"小机动目标的亚阈值检测与跟踪","authors":"V. Koshelev, V. Belokurov","doi":"10.1109/CRMICO.2014.6959794","DOIUrl":null,"url":null,"abstract":"The present paper concerns the feasibility of applying a combination of Gaussian particle filter and an interactive multimode filter for mutual detection and tracking of a small maneuvering target. We propose using two models: one with a constant velocity, another with a constant acceleration. Filters, corresponding to each of those models, employ Gaussian particle filter. Numerical modeling results demonstrate that the proposed algorithm is capable of detecting a maneuvering target at low signal-to-noise ratio.","PeriodicalId":6662,"journal":{"name":"2014 24th International Crimean Conference Microwave & Telecommunication Technology","volume":"18 1","pages":"1131-1132"},"PeriodicalIF":0.0000,"publicationDate":"2014-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Subthreshold detection and tracking of a small maneuvering target\",\"authors\":\"V. Koshelev, V. Belokurov\",\"doi\":\"10.1109/CRMICO.2014.6959794\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The present paper concerns the feasibility of applying a combination of Gaussian particle filter and an interactive multimode filter for mutual detection and tracking of a small maneuvering target. We propose using two models: one with a constant velocity, another with a constant acceleration. Filters, corresponding to each of those models, employ Gaussian particle filter. Numerical modeling results demonstrate that the proposed algorithm is capable of detecting a maneuvering target at low signal-to-noise ratio.\",\"PeriodicalId\":6662,\"journal\":{\"name\":\"2014 24th International Crimean Conference Microwave & Telecommunication Technology\",\"volume\":\"18 1\",\"pages\":\"1131-1132\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-11-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 24th International Crimean Conference Microwave & Telecommunication Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CRMICO.2014.6959794\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 24th International Crimean Conference Microwave & Telecommunication Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CRMICO.2014.6959794","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Subthreshold detection and tracking of a small maneuvering target
The present paper concerns the feasibility of applying a combination of Gaussian particle filter and an interactive multimode filter for mutual detection and tracking of a small maneuvering target. We propose using two models: one with a constant velocity, another with a constant acceleration. Filters, corresponding to each of those models, employ Gaussian particle filter. Numerical modeling results demonstrate that the proposed algorithm is capable of detecting a maneuvering target at low signal-to-noise ratio.