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Two Effective Strategies for Complex Domain Compressive Sensing

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Abstract

In this paper, we propose two novel recovery schemes for complex domain compressive sensing. Firstly, we present a new strategy to separate the real and imaginary parts of a complex signal for \(\ell _{1}\) minimization. While the method is simple, simulation results show that it is quite efficient because it reduces the sampling rate. Secondly, the least squares (LS) sub-problem is a key part of the orthogonal matching pursuit (OMP) algorithm and accounts for a large part of the computational load. We employ the Landweber algorithm to efficiently solve the LS problem. Furthermore, we propose four new parameter options to accelerate the convergence. Our numerical experiments show that our method is competitive with the pseudo-inverse.

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Author information

Authors and Affiliations

  1. School of Science, Beijing Jiaotong University, Beijing, 100044, China

    Ruirui Kang & Gangrong Qu

  2. Key Laboratory of Precision Opto-mechatronics Technology of the Ministry of Education, Beihang University, Beijing, 100191, China

    Bin Wang

  3. Beijing Center for Mathematics and Information Interdisciplinary Sciences, Beijing, 100048, China

    Gangrong Qu

Authors
  1. Ruirui Kang
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  2. Gangrong Qu
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  3. Bin Wang
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Corresponding author

Correspondence to Gangrong Qu.

Additional information

Partially supported by the National Natural Science Foundation of China (61071144; 61271012).

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Kang, R., Qu, G. & Wang, B. Two Effective Strategies for Complex Domain Compressive Sensing. Circuits Syst Signal Process 35, 3380–3392 (2016). https://doi.org/10.1007/s00034-015-0202-6

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  • DOI: https://doi.org/10.1007/s00034-015-0202-6

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