Proceedings Article10.1109/CSPA.2013.6530043
Improved performance compressed sensing based pulse Doppler radar
T. Ikram,Muhammad Salman +1 more
- 08 Mar 2013
- pp 209-214
2
TL;DR: Improved performance compressed sensing based pulse Doppler radar based Orthogonal Matching Pursuit (OMP) method with Fourier dictionary at low signal to noise ratio (SNR) is presented.
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Abstract: In this paper, we present improved performance compressed sensing (CS) based pulse Doppler radar. CS is an emerging sampling technique which uses very few number of samples to reconstruct original signal. Improvement in performance is achieved using Orthogonal Matching Pursuit (OMP) method with Fourier dictionary at low signal to noise ratio (SNR). OMP is an iterative and fast reconstruction method while Fourier dictionary is used to transform the original signal from time domain to Fourier domain. A significant reduction of sampling frequency by a factor of 360 is achieved at SNR as low as 10 dB, while preserving essential signal information. Low signal reconstruction error leads to good detection performance using Generalized Likelihood Ratio Test (GLRT), a detector based on ratio of probabilities of detection and false alarm. Simulation results demonstrate usefulness of the ideas presented.
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Stéphane Mallat,Zhifeng Zhang +1 more
TL;DR: The authors introduce an algorithm, called matching pursuit, that decomposes any signal into a linear expansion of waveforms that are selected from a redundant dictionary of functions, chosen in order to best match the signal structures.
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Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
Joel A. Tropp,Anna C. Gilbert +1 more
TL;DR: It is demonstrated theoretically and empirically that a greedy algorithm called orthogonal matching pursuit (OMP) can reliably recover a signal with m nonzero entries in dimension d given O(m ln d) random linear measurements of that signal.
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Gitta Kutyniok
- 15 Mar 2012
TL;DR: Machine generated contents note: Introduction to compressed sensing Mark A. Davenport, Marco F. Duarte, Yonina C. Eldar, Pier Luigi Dragotta and Zvika Ben-Haim.
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Stable signal recovery from incomplete and inaccurate measurements
TL;DR: In this paper, the authors considered the problem of recovering a vector x ∈ R^m from incomplete and contaminated observations y = Ax ∈ e + e, where e is an error term.
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