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dc.contributor.authorSevimli, R. Akınen_US
dc.contributor.authorÇetin, A. Enisen_US
dc.coverage.spatialBaltimore, Maryland, United Statesen_US
dc.date.accessioned2016-02-08T12:11:20Zen_US
dc.date.available2016-02-08T12:11:20Zen_US
dc.date.issued2015en_US
dc.identifier.issn0277-786Xen_US
dc.identifier.urihttp://hdl.handle.net/11693/28108en_US
dc.descriptionDate of Conference: 20-24 April 2015en_US
dc.descriptionConference Name: SPIE Sensing Technology Applications, 2015en_US
dc.description.abstractPassive Bistatic Radar (PBR) systems use illuminators of opportunity, such as FM, TV, and DAB broadcasts. The most common illuminator of opportunity used in PBR systems is the FM radio stations. Single FM channel based PBR systems do not have high range resolution and may turn out to be noisy. In order to enhance the range resolution of the PBR systems algorithms using several FM channels at the same time are proposed. In standard methods, consecutive FM channels are translated to baseband as is and fed to the matched filter to compute the range-Doppler map. Multichannel FM based PBR systems have better range resolution than single channel systems. However superious sidelobe peaks occur as a side effect. In this article, we linearly predict the surveillance signal using the modulated and delayed reference signal components. We vary the modulation frequency and the delay to cover the entire range-Doppler plane. Whenever there is a target at a specific range value and Doppler value the prediction error is minimized. The cost function of the linear prediction equation has three components. The first term is the real-part of the ordinary least squares term, the second-Term is the imaginary part of the least squares and the third component is the l2-norm of the prediction coefficients. Separate minimization of real and imaginary parts reduces the side lobes and decrease the noise level of the range-Doppler map. The third term enforces the sparse solution on the least squares problem. We experimentally observed that this approach is better than both the standard least squares and other sparse least squares approaches in terms of side lobes. Extensive simulation examples will be presented in the final form of the paper.en_US
dc.language.isoEnglishen_US
dc.source.titleProceedings of SPIE Vol. 9484, Compressive Sensing IVen_US
dc.relation.isversionofhttp://dx.doi.org/10.1117/12.2176764en_US
dc.subjectCLSen_US
dc.subjectCorrelation coefficienten_US
dc.subjectLeast squaresen_US
dc.subjectMultichannel FMen_US
dc.subjectPassive bistatic radaren_US
dc.subjectSide lobesen_US
dc.subjectChlorineen_US
dc.subjectCompressed sensingen_US
dc.subjectCost functionsen_US
dc.subjectForecastingen_US
dc.subjectMatched filtersen_US
dc.subjectPattern matchingen_US
dc.subjectRadar systemsen_US
dc.titleCross-term free based bistatic radar system using sparse least squaresen_US
dc.typeConference Paperen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.citation.spage1en_US
dc.citation.epage13en_US
dc.citation.volumeNumber9484en_US
dc.identifier.doi10.1117/12.2176764en_US
dc.publisherSPIEen_US
dc.contributor.bilkentauthorÇetin, A. Enis


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