Browsing by Keywords "Sparse signals"

Now showing items 1-6 of 6

    • Average error in recovery of sparse signals and discrete fourier transform 

      Özçelikkale, Ayça; Yüksel, S.; Özaktaş Haldun M. (IEEE, 2012-04)
      In compressive sensing framework it has been shown that a sparse signal can be successfully recovered from a few random measurements. The Discrete Fourier Transform (DFT) is one of the transforms that provide the best ...

    • Compressive sensing based target detection in delay-doppler radars 

      Teke O.; Arikan, O.; Gürbüz, A.C. (2013)
      Compressive Sensing theory shows that, a sparse signal can be reconstructed from its sub-Nyquist rate random samples. With this property, CS approach has many applications. Radar systems, which deal with sparse signal due ...

    • Filtered Variation method for denoising and sparse signal processing 

      Kose, K.; Cevher V.; Cetin, A.E. (2012)
      We propose a new framework, called Filtered Variation (FV), for denoising and sparse signal processing applications. These problems are inherently ill-posed. Hence, we provide regularization to overcome this challenge by ...

    • A new OMP technique for sparse recovery 

      Teke O.; Gürbüz, A.C.; Arikan, O. (2012)
      Compressive Sensing (CS) theory details how a sparsely represented signal in a known basis can be reconstructed using less number of measurements. However in reality there is a mismatch between the assumed and the actual ...

    • Phase retrieval of sparse signals from Fourier Transform magnitude using non-negative matrix factorization 

      Salman, M.S.; Eleyan, A.; Deprem, Z.; Cetin, A.E. (2013)
      Signal and image reconstruction from Fourier Transform magnitude is a difficult inverse problem. Fourier transform magnitude can be measured in many practical applications, but the phase may not be measured. Since the ...

    • Range-Doppler radar target detection using compressive sensing 

      Sevimli, R.A.; Tofighi, M.; Cetin, A.E. (IEEE Computer Society, 2014)
      Compressive sensing (CS) idea enables the reconstruction of a sparse signal from small number of measurements. CS approach has many applications in many areas. One of the areas is radar systems. In this article, the radar ...