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Particle Swarm Optimization of Compression Measurement for Signal Detection

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Abstract

In this paper, the random matrix in the compressive and subspace compressive detectors is optimized based on the particle swarm optimization (PSO). The PSO, which belongs to swarm intelligent theory, is used for the first time to solve the optimization problem of the random projection matrix, leading to an improved version of the conventional compressive and subspace compressive detectors. Simulation results show the proposed PSO-based detectors can achieve a better detection performance and require fewer measurements than the traditional compressive detectors without using PSO.

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Authors and Affiliations

  1. College of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China

    Yongjie Li, Rongfang Song & Wennai Wang

  2. National Mobile Communication Research Laboratory, Southeast University, Nanjing, 210096, China

    Rongfang Song

Authors
  1. Yongjie Li
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  2. Rongfang Song
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  3. Wennai Wang
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Correspondence to Rongfang Song.

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Li, Y., Song, R. & Wang, W. Particle Swarm Optimization of Compression Measurement for Signal Detection. Circuits Syst Signal Process 31, 1109–1126 (2012). https://doi.org/10.1007/s00034-011-9371-0

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  • DOI: https://doi.org/10.1007/s00034-011-9371-0

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