B. Jameson, D. Garmatyuk, Y. Morton, A. Curtis, R. Ewing
{"title":"Target scene reconstruction in indoor environment with cognitive OFDM radar","authors":"B. Jameson, D. Garmatyuk, Y. Morton, A. Curtis, R. Ewing","doi":"10.1109/WDD.2012.7311259","DOIUrl":null,"url":null,"abstract":"In this paper we discuss the design and experimental results associated with the software-defined ultra-wideband (UWB) orthogonal frequency division multiplexing (OFDM) system, which we aim to configure as a cognitive radar. Frequency-domain likelihood ratios (LR) are obtained via response distribution modeling and statistical parameter estimation on a per-sub-carrier basis. Then the generalized likelihood ratio test (GLRT) is performed on a composite LR. We propose to enhance this approach via adaptive selection of OFDM signal's sub-carriers to improve the detection performance; exploiting frequency diversity of the target scene by complementing the GLRT-based detection with frequency profile matching. This approach, when applied to a software-defined system, will provide for cognitive radar functionality.","PeriodicalId":102625,"journal":{"name":"2012 International Waveform Diversity & Design Conference (WDD)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 International Waveform Diversity & Design Conference (WDD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WDD.2012.7311259","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
In this paper we discuss the design and experimental results associated with the software-defined ultra-wideband (UWB) orthogonal frequency division multiplexing (OFDM) system, which we aim to configure as a cognitive radar. Frequency-domain likelihood ratios (LR) are obtained via response distribution modeling and statistical parameter estimation on a per-sub-carrier basis. Then the generalized likelihood ratio test (GLRT) is performed on a composite LR. We propose to enhance this approach via adaptive selection of OFDM signal's sub-carriers to improve the detection performance; exploiting frequency diversity of the target scene by complementing the GLRT-based detection with frequency profile matching. This approach, when applied to a software-defined system, will provide for cognitive radar functionality.