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논문검색

Compressive Sampling Orthogonal Matching Pursuit Algorithm Based on Peak Signal to Noise Ratio

초록

영어

In order to improve signal reconstruction accuracy, a CoSaMP (Compressive Sampling Matching Pursuit) algorithm based on peak signal to noise ratio is proposed in allusion to the disadvantages of CoSaMP algorithm. Firstly, the discrete cosine wave transform is improved to initially estimate the signal sparseness; secondly, the optimum iteration number is determined according to the peak signal to noise ratio to gradually approach to the real sparseness of the signal for signal reconstruction; finally, the simulation experiment is adopted to analyze the algorithm performance. The result shows: compared with CoSaMP algorithm and other improved CoSaMP algorithms, the proposed algorithm can not only obtain more ideal reconstruction effect, but also improve the reconstruction success probability and reduce the reconstruction time, thus having higher reconstruction efficiency.

목차

Abstract
 1. Introduction
 2. Compressive Sensing Theory
 3. Improved CoSaMP Algorithm
  3.1. CoSaMP Algorithm
  3.2. Improvement of CoSaMP Algorithm
 4. Simulation Experiment
  4.1. Simulation Environment
  4.2. Result Analysis
 5. Conclusion
 Acknowledgement
 References

저자정보

  • Hu Dan College of Big Data and Information Engineering, Guizhou University, Guiyang Guizhou, 550025, China

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