{"title":"基于小波变换的无源雷达目标跟踪","authors":"F. Farhad Zadeh, H. Amindavar","doi":"10.1109/SAM.2008.4606916","DOIUrl":null,"url":null,"abstract":"In this paper, we utilize chirplet transformation to estimate the differential delays-Dopplers in an array of sensors. After chirplet modeling of the received signals from each sensor we use extended Kalman filtering (EKF) for tracking the targets by estimating the differential delays and differential Dopplers. This new approach is particularly useful in passive radar and sonar for target tracking. Chirplet modeling is crucial since the received signals are non-stationary in nature.","PeriodicalId":422747,"journal":{"name":"2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop","volume":"340 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Passive radar target tracking using chirplet transform\",\"authors\":\"F. Farhad Zadeh, H. Amindavar\",\"doi\":\"10.1109/SAM.2008.4606916\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we utilize chirplet transformation to estimate the differential delays-Dopplers in an array of sensors. After chirplet modeling of the received signals from each sensor we use extended Kalman filtering (EKF) for tracking the targets by estimating the differential delays and differential Dopplers. This new approach is particularly useful in passive radar and sonar for target tracking. Chirplet modeling is crucial since the received signals are non-stationary in nature.\",\"PeriodicalId\":422747,\"journal\":{\"name\":\"2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop\",\"volume\":\"340 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-07-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SAM.2008.4606916\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SAM.2008.4606916","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Passive radar target tracking using chirplet transform
In this paper, we utilize chirplet transformation to estimate the differential delays-Dopplers in an array of sensors. After chirplet modeling of the received signals from each sensor we use extended Kalman filtering (EKF) for tracking the targets by estimating the differential delays and differential Dopplers. This new approach is particularly useful in passive radar and sonar for target tracking. Chirplet modeling is crucial since the received signals are non-stationary in nature.
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