CS radar imaging via adaptive CAMP

Laura Anitori, Matern Otten, Peter Hoogeboom, Arian Maleki, Richard Baraniuk

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


In this paper we present results on application of Compressive Sensing (CS) to high resolution radar imaging and propose the adaptive Complex Approximate Message Passing (CAMP) algorithm for image reconstruction. CS provides a theoretical framework that guarantees, under certain assumptions, reconstruction of sparse signals from many fewer measurements than required by the Nyquist-Shannon sampling theorem. However, whereas most conventional imaging techniques are based on linear filtering, in CS the image is obtained from a subsampled set of measurements by means of a non-linear reconstruction algorithm. A variety of such algorithms have been proposed, and, for a given problem instance, the solution will depend on a threshold that has either to be provided by the user or estimated from the compressed measurements. In this paper, we present an adaptive version of CAMP, where the threshold is estimated from the data itself to provide a solution with minimum reconstruction error. Our results show that the adaptive CAMP algorithm can reconstruct the image with a Mean Squared Error (MSE) comparable to the reconstruction error achieved using an optimally tuned algorithm.

Original languageEnglish (US)
Title of host publicationEUSAR 2012; 9th European Conference on Synthetic Aperture Radar
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Electronic)9783800734047
StatePublished - 2012
Event9th European Conference on Synthetic Aperture Radar, EUSAR 2012 - Nuremberg, Germany
Duration: Apr 23 2012Apr 26 2012

Publication series

NameProceedings of the European Conference on Synthetic Aperture Radar, EUSAR
ISSN (Print)2197-4403


Other9th European Conference on Synthetic Aperture Radar, EUSAR 2012

ASJC Scopus subject areas

  • Signal Processing
  • Instrumentation


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