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Proceedings Article

Detection performance of radar compressive sensing in noisy environments

[+] Author Affiliations
Asmita Korde, Tinoosh Mohsenin

Univ. of Maryland, Baltimore County (United States)

Damon Bradley

NASA Goddard Space Flight Ctr. (United States)

Proc. SPIE 8717, Compressive Sensing II, 87170L (May 31, 2013); doi:10.1117/12.2016209
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From Conference Volume 8717

  • Compressive Sensing II
  • Fauzia Ahmad
  • Baltimore, Maryland, USA | April 29, 2013

abstract

In this paper, radar detection via compressive sensing is explored. Compressive sensing is a new theory of sampling which allows the reconstruction of a sparse signal by sampling at a much lower rate than the Nyquist rate. By using this technique in radar, the use of matched filter can be eliminated and high rate sampling can be replaced with low rate sampling. In this paper, compressive sensing is analyzed by applying varying factors such as noise and different measurement matrices. Different reconstruction algorithms are compared by generating ROC curves to determine their detection performance. We conduct simulations for a 64-length signal with 3 targets to determine the effectiveness of each algorithm in varying SNR. We also propose a simplified version of Orthogonal Matching Pursuit (OMP). Through numerous simulations, we find that a simplified version of Orthogonal Matching Pursuit (OMP), can give better results than the original OMP in noisy environments when sparsity is highly over estimated, but does not work as well for low noise environments. © (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation

Asmita Korde ; Damon Bradley and Tinoosh Mohsenin
" Detection performance of radar compressive sensing in noisy environments ", Proc. SPIE 8717, Compressive Sensing II, 87170L (May 31, 2013); doi:10.1117/12.2016209; http://dx.doi.org/10.1117/12.2016209


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