{"title":"Commensal radar: Range-Doppler processing using a recursive DFT","authors":"M. Inggs, A. van der Byl, C. Tong","doi":"10.1109/RADAR.2013.6652001","DOIUrl":null,"url":null,"abstract":"Commensal radar [1] is an attractive solution to low-cost air-traffic surveillance. This paper proposes an alternative approach using a recursive Fourier transform for Doppler processing for computing amplitude-range-Doppler (ARD) data. At present, the full FFT is utilsed, however, at the cost of unnecessary channel decomposition, excess memory, and high-overhead for the required sampling window lasting 1-4s. This work proposes the use of a recursive Fourier technique which allows select channels to be computed, introduces significant memory savings, and offers very fine time frequency decomposition.","PeriodicalId":365285,"journal":{"name":"2013 International Conference on Radar","volume":"68 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 International Conference on Radar","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RADAR.2013.6652001","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 10
Abstract
Commensal radar [1] is an attractive solution to low-cost air-traffic surveillance. This paper proposes an alternative approach using a recursive Fourier transform for Doppler processing for computing amplitude-range-Doppler (ARD) data. At present, the full FFT is utilsed, however, at the cost of unnecessary channel decomposition, excess memory, and high-overhead for the required sampling window lasting 1-4s. This work proposes the use of a recursive Fourier technique which allows select channels to be computed, introduces significant memory savings, and offers very fine time frequency decomposition.