{"title":"脉冲多普勒雷达在地面杂波存在下识别运动地面目标","authors":"M. Alaee, H. Amindavar, A. Reza","doi":"10.1109/UKSIM.2008.118","DOIUrl":null,"url":null,"abstract":"In this paper we propose an automatic target recognitionalgorithm to recognize and distinguish of three classes oftargets: personnel, wheeled vehicles and animals, based on thedoppler signatures they produce when moving, using a low-resolutionground surveillance pulse doppler RADAR. Using signal processingtechniques such as the short-time Fourier transform (STFT)and the chirplet transform, various parameters of the targets canbe extracted from the signal. This paper proposed classification ofmoving targets with use of the chirplet transform. The algorithmwas trained and tested on real radar signatures of multiple examplesof moving targets from each class and the performance wasshown to be invariant to target speed and orientation.","PeriodicalId":22356,"journal":{"name":"Tenth International Conference on Computer Modeling and Simulation (uksim 2008)","volume":"69 1","pages":"23-27"},"PeriodicalIF":0.0000,"publicationDate":"2008-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Recognition of Moving Terrestrial Targets in the Presence of Terrestrial Clutters with a Pulse Doppler RADAR\",\"authors\":\"M. Alaee, H. Amindavar, A. Reza\",\"doi\":\"10.1109/UKSIM.2008.118\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we propose an automatic target recognitionalgorithm to recognize and distinguish of three classes oftargets: personnel, wheeled vehicles and animals, based on thedoppler signatures they produce when moving, using a low-resolutionground surveillance pulse doppler RADAR. Using signal processingtechniques such as the short-time Fourier transform (STFT)and the chirplet transform, various parameters of the targets canbe extracted from the signal. This paper proposed classification ofmoving targets with use of the chirplet transform. The algorithmwas trained and tested on real radar signatures of multiple examplesof moving targets from each class and the performance wasshown to be invariant to target speed and orientation.\",\"PeriodicalId\":22356,\"journal\":{\"name\":\"Tenth International Conference on Computer Modeling and Simulation (uksim 2008)\",\"volume\":\"69 1\",\"pages\":\"23-27\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Tenth International Conference on Computer Modeling and Simulation (uksim 2008)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/UKSIM.2008.118\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Tenth International Conference on Computer Modeling and Simulation (uksim 2008)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/UKSIM.2008.118","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Recognition of Moving Terrestrial Targets in the Presence of Terrestrial Clutters with a Pulse Doppler RADAR
In this paper we propose an automatic target recognitionalgorithm to recognize and distinguish of three classes oftargets: personnel, wheeled vehicles and animals, based on thedoppler signatures they produce when moving, using a low-resolutionground surveillance pulse doppler RADAR. Using signal processingtechniques such as the short-time Fourier transform (STFT)and the chirplet transform, various parameters of the targets canbe extracted from the signal. This paper proposed classification ofmoving targets with use of the chirplet transform. The algorithmwas trained and tested on real radar signatures of multiple examplesof moving targets from each class and the performance wasshown to be invariant to target speed and orientation.