P. Rakovic, M. Daković, L. Stanković, Thayaparan Thayananthan
{"title":"An algorithm for detecting a maneuvering target based on TFR and Viterbi algorithm","authors":"P. Rakovic, M. Daković, L. Stanković, Thayaparan Thayananthan","doi":"10.1109/SM2ACD.2010.5672320","DOIUrl":null,"url":null,"abstract":"An algorithm for radar signal detection method based on a time-frequency representation (TFR) and Viterbi algorithm (VA) is proposed. Two TFR methods are considered, Short-time Fourier Transform (STFT) and S-method (SM). Performance and detection ability of the proposed algorithm was investigated on real radar signals of a single manuvering target with added Gaussian noise and signal-to-noise ratio from −15 dB to −2 dB. The results show that the proposed algorithm works well even in very low SNR values.","PeriodicalId":442381,"journal":{"name":"2010 XIth International Workshop on Symbolic and Numerical Methods, Modeling and Applications to Circuit Design (SM2ACD)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 XIth International Workshop on Symbolic and Numerical Methods, Modeling and Applications to Circuit Design (SM2ACD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SM2ACD.2010.5672320","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
An algorithm for radar signal detection method based on a time-frequency representation (TFR) and Viterbi algorithm (VA) is proposed. Two TFR methods are considered, Short-time Fourier Transform (STFT) and S-method (SM). Performance and detection ability of the proposed algorithm was investigated on real radar signals of a single manuvering target with added Gaussian noise and signal-to-noise ratio from −15 dB to −2 dB. The results show that the proposed algorithm works well even in very low SNR values.
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