Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/81224
Title: Towards compressed sensing for ground-to-air monostatic radar
Authors: Dauwels, Justin
Kannan, Srinivasan
Keywords: Remote sensing
Issue Date: 2013
Source: Dauwels, J., & Kannan, S. (2013). Towards compressed sensing for ground-to-air monostatic radar. 3rd SONDRA workshop on EM modeling, New Concepts and Signal Processing for Radar Detection and Remote Sensing.
Abstract: Recently, it is shown that the fundamental problem of rangeDoppler estimation can be solved efficiently by compressed sensing (CS) from single-pulse radar return. The performance of CS radar particularly degrade significantly with noise and hence the primary concern is to determine the regime where the operation of CS radar is satisfactory. Most of the studies on CS radar are often conducted under unrealistic conditions where the SNR is much higher than in typical radar applications (e.g., SNR > 5dB). In this paper, we investigate how to improve the CS reconstruction by using coherent integration over N pulses. We consider two scenarios: i) coherent integration is performed before CS reconstruction; ii) coherent integration is performed after CS reconstruction. We provide numerical results for both scenarios, and demonstrate that a proportional reduction in reconstruction error is obtained if coherent integration is carried out before CS reconstruction, corresponding to an effective gain of SNRg = 10 log10 N. On the other hand, when coherent integration is performed after applying CS reconstruction to single pulse radar returns, there is negligible gain. Both observations can be explained by the fact that CS reconstruction exhibits a threshold phenomenon with regard to SNR. By boosting the effective SNR through coherent integration, one can obtain more reliable CS reconstruction.
URI: https://hdl.handle.net/10356/81224
http://hdl.handle.net/10220/40678
Rights: © 2013 3rd SONDRA Workshop on EM Modeling, New Concepts and Signal Processing for Radar Detection and Remote Sensing. This is the author created version of a work that has been peer reviewed and accepted for publication by 3rd SONDRA Workshop on EM Modeling, New Concepts and Signal Processing for Radar Detection and Remote Sensing, 3rd SONDRA Workshop on EM Modeling, New Concepts and Signal Processing for Radar Detection and Remote Sensing. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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