{"title":"一种用于弱小目标检测的高效频域速度滤波实现","authors":"H. L. Kennedy","doi":"10.1109/DICTA.2010.16","DOIUrl":null,"url":null,"abstract":"An efficient Fourier-domain implementation of the velocity filter is presented. The Sliding Discrete Fourier Transform (SDFT) is exploited to yield a Track-Before-Detect (TBD) algorithm with a complexity that is independent of the filter integration time. As a consequence, dim targets near the noise floor of acquisition or surveillance sensors may be detected, and their states estimated, at a relatively low computational cost. The performance of the method is demonstrated using real sensor data. When processing the acquired data, the SDFT implementation is approximately 3 times faster than the equivalent Fast Fourier Transform (FFT) implementation and 16 times faster than the corresponding spatiotemporal implementation.","PeriodicalId":246460,"journal":{"name":"2010 International Conference on Digital Image Computing: Techniques and Applications","volume":"167 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"An Efficient Frequency-Domain Velocity-Filter Implementation for Dim Target Detection\",\"authors\":\"H. L. Kennedy\",\"doi\":\"10.1109/DICTA.2010.16\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An efficient Fourier-domain implementation of the velocity filter is presented. The Sliding Discrete Fourier Transform (SDFT) is exploited to yield a Track-Before-Detect (TBD) algorithm with a complexity that is independent of the filter integration time. As a consequence, dim targets near the noise floor of acquisition or surveillance sensors may be detected, and their states estimated, at a relatively low computational cost. The performance of the method is demonstrated using real sensor data. When processing the acquired data, the SDFT implementation is approximately 3 times faster than the equivalent Fast Fourier Transform (FFT) implementation and 16 times faster than the corresponding spatiotemporal implementation.\",\"PeriodicalId\":246460,\"journal\":{\"name\":\"2010 International Conference on Digital Image Computing: Techniques and Applications\",\"volume\":\"167 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 International Conference on Digital Image Computing: Techniques and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DICTA.2010.16\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 International Conference on Digital Image Computing: Techniques and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DICTA.2010.16","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Efficient Frequency-Domain Velocity-Filter Implementation for Dim Target Detection
An efficient Fourier-domain implementation of the velocity filter is presented. The Sliding Discrete Fourier Transform (SDFT) is exploited to yield a Track-Before-Detect (TBD) algorithm with a complexity that is independent of the filter integration time. As a consequence, dim targets near the noise floor of acquisition or surveillance sensors may be detected, and their states estimated, at a relatively low computational cost. The performance of the method is demonstrated using real sensor data. When processing the acquired data, the SDFT implementation is approximately 3 times faster than the equivalent Fast Fourier Transform (FFT) implementation and 16 times faster than the corresponding spatiotemporal implementation.