31 May 2013 Detection performance of radar compressive sensing in noisy environments
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Proceedings Volume 8717, Compressive Sensing II; 87170L (2013); doi: 10.1117/12.2016209
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States
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.
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Asmita Korde, Damon Bradley, Tinoosh Mohsenin, "Detection performance of radar compressive sensing in noisy environments", Proc. SPIE 8717, Compressive Sensing II, 87170L (31 May 2013); doi: 10.1117/12.2016209; http://dx.doi.org/10.1117/12.2016209
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KEYWORDS
Signal to noise ratio

Reconstruction algorithms

Radar

Compressed sensing

Signal detection

Detection and tracking algorithms

Matrices

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