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Proceedings Paper

Cross-term free based bistatic radar system using sparse least squares
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Paper Abstract

Passive 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.

Paper Details

Date Published: 19 May 2015
PDF: 13 pages
Proc. SPIE 9484, Compressive Sensing IV, 948404 (19 May 2015); doi: 10.1117/12.2176764
Show Author Affiliations
R. Akin Sevimli, Bilkent Univ. (Turkey)
A. Enis Cetin, Bilkent Univ. (Turkey)

Published in SPIE Proceedings Vol. 9484:
Compressive Sensing IV
Fauzia Ahmad, Editor(s)

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