Vol. 73
Latest Volume
All Volumes
PIERC 142 [2024] PIERC 141 [2024] PIERC 140 [2024] PIERC 139 [2024] PIERC 138 [2023] PIERC 137 [2023] PIERC 136 [2023] PIERC 135 [2023] PIERC 134 [2023] PIERC 133 [2023] PIERC 132 [2023] PIERC 131 [2023] PIERC 130 [2023] PIERC 129 [2023] PIERC 128 [2023] PIERC 127 [2022] PIERC 126 [2022] PIERC 125 [2022] PIERC 124 [2022] PIERC 123 [2022] PIERC 122 [2022] PIERC 121 [2022] PIERC 120 [2022] PIERC 119 [2022] PIERC 118 [2022] PIERC 117 [2021] PIERC 116 [2021] PIERC 115 [2021] PIERC 114 [2021] PIERC 113 [2021] PIERC 112 [2021] PIERC 111 [2021] PIERC 110 [2021] PIERC 109 [2021] PIERC 108 [2021] PIERC 107 [2021] PIERC 106 [2020] PIERC 105 [2020] PIERC 104 [2020] PIERC 103 [2020] PIERC 102 [2020] PIERC 101 [2020] PIERC 100 [2020] PIERC 99 [2020] PIERC 98 [2020] PIERC 97 [2019] PIERC 96 [2019] PIERC 95 [2019] PIERC 94 [2019] PIERC 93 [2019] PIERC 92 [2019] PIERC 91 [2019] PIERC 90 [2019] PIERC 89 [2019] PIERC 88 [2018] PIERC 87 [2018] PIERC 86 [2018] PIERC 85 [2018] PIERC 84 [2018] PIERC 83 [2018] PIERC 82 [2018] PIERC 81 [2018] PIERC 80 [2018] PIERC 79 [2017] PIERC 78 [2017] PIERC 77 [2017] PIERC 76 [2017] PIERC 75 [2017] PIERC 74 [2017] PIERC 73 [2017] PIERC 72 [2017] PIERC 71 [2017] PIERC 70 [2016] PIERC 69 [2016] PIERC 68 [2016] PIERC 67 [2016] PIERC 66 [2016] PIERC 65 [2016] PIERC 64 [2016] PIERC 63 [2016] PIERC 62 [2016] PIERC 61 [2016] PIERC 60 [2015] PIERC 59 [2015] PIERC 58 [2015] PIERC 57 [2015] PIERC 56 [2015] PIERC 55 [2014] PIERC 54 [2014] PIERC 53 [2014] PIERC 52 [2014] PIERC 51 [2014] PIERC 50 [2014] PIERC 49 [2014] PIERC 48 [2014] PIERC 47 [2014] PIERC 46 [2014] PIERC 45 [2013] PIERC 44 [2013] PIERC 43 [2013] PIERC 42 [2013] PIERC 41 [2013] PIERC 40 [2013] PIERC 39 [2013] PIERC 38 [2013] PIERC 37 [2013] PIERC 36 [2013] PIERC 35 [2013] PIERC 34 [2013] PIERC 33 [2012] PIERC 32 [2012] PIERC 31 [2012] PIERC 30 [2012] PIERC 29 [2012] PIERC 28 [2012] PIERC 27 [2012] PIERC 26 [2012] PIERC 25 [2012] PIERC 24 [2011] PIERC 23 [2011] PIERC 22 [2011] PIERC 21 [2011] PIERC 20 [2011] PIERC 19 [2011] PIERC 18 [2011] PIERC 17 [2010] PIERC 16 [2010] PIERC 15 [2010] PIERC 14 [2010] PIERC 13 [2010] PIERC 12 [2010] PIERC 11 [2009] PIERC 10 [2009] PIERC 9 [2009] PIERC 8 [2009] PIERC 7 [2009] PIERC 6 [2009] PIERC 5 [2008] PIERC 4 [2008] PIERC 3 [2008] PIERC 2 [2008] PIERC 1 [2008]
2017-04-04
Compressive Sensing Reconstruction of Wideband Antenna Radiation Characteristics
By
Progress In Electromagnetics Research C, Vol. 73, 1-8, 2017
Abstract
Characterization measurements of wideband antennas can be a time intensive and an expensive process as many data points are required in both the angular and frequency dimensions. Parallel compressive sensing is proposed to reconstruct the radiation-frequency patterns (RFP) of antennas from a sparse and random set of measurements. The modeled RFP of the dual-ridge horn, bicone, and Vivaldi antennas are used to analyze the minimum number of measurements needed for reconstruction, the difference in uniform versus non-uniform reconstruction, and the sparsity transform function used in the compressive sensing algorithm. The effect of additive white Gaussian noise (AWGN) on the minimum number of data points required for reconstruction is also studied. In a noise-free environment, the RFP of the antennas were adequately reconstructed using as little as 33% of the original data points. It was found that the RFPs were adequately reconstructed with less data points when the discrete cosine transforms (DCT), rather than discrete Fourier transforms (DFT) was used in the compressive sensing algorithm. The presence of noise increases the number of data points required to reconstruct an RFP to a specified error tolerance, but the antenna RFPs can be reconstructed to within 1% root-mean-square-error of the original with a signal to noise ratio as high as -15 dB. The use of compressive sensing can thus lead to a new measurement methodology whereby a small subset of the total angular and frequency measurements is taken at random, and a full reconstruction of radiation and frequency behavior of the antenna is achieved during post-processing.
Citation
Patrick Debroux, and Berenice Verdin, "Compressive Sensing Reconstruction of Wideband Antenna Radiation Characteristics," Progress In Electromagnetics Research C, Vol. 73, 1-8, 2017.
doi:10.2528/PIERC16120605
References

1. Miller, E. K., "Using adaptive estimation to minimize the number of samples needed to develop a radiation or scattering pattern to a specified uncertainty," ACES Journal, Vol. 17, No. 3, 176-185, 2002.

2. Werner, D. H. and R. J. Allard, "The simultaneous interpolation of antenna radiation patterns in both the spatial and frequency domains using model-based parameter estimation," IEEE Transactions on Antennas and Propagation, Vol. 48, No. 3, 383-392, March 2000.
doi:10.1109/8.841899

3. Martı-Canales, J. and L. P. Lighart, "Reconstruction of measured antenna patterns and related time-varying aperture fields," IEEE Transactions on Antennas and Propagation, Vol. 52, No. 11, 3143-3147, November 2004.
doi:10.1109/TAP.2004.835235

4. Tkadlec, R. and Z. Novacek, "Radiation pattern reconstruction from the near-field amplitude measurement on two planes using PSO," Radioengineering, Vol. 14, No. 4, 63-67, December 2005.

5. Rammal, R., M. Lalande, M. Jouvet, N. Feix, J. Andrieu, and B. Jecko, "Far-field reconstruction from transient near-field measurements using cylindrical modal development," International Journal of Antennas and Propagation, Article ID 798473, 2009.

6. Koh, J., A. De, T. K. Sarkar, H. Moon, W. Zhao, and M. Salazar-Palma, "Free space radiation pattern reconstruction from non-anechoic measurements using an impulse response of the environment," IEEE Transactions on Antennas and Propagation, Vol. 60, No. 2, 821-831, February 2012.
doi:10.1109/TAP.2011.2173117

7. Wei, S.-J., X.-L. Zhang, J. Shi, and K.-F. Liao, "Sparse array microwave 3-D imaging: Compressed sensing recovery and experimental study," Progress In Electromagnetic Research, Vol. 135, 161-181, 2013.
doi:10.2528/PIER12082305

8. Verdin, B. and P. Debroux, "2D and 3D far-field radiation patterns reconstruction based on compressive sensing," Progress In Electromagnetic Research M, Vol. 46, 47-56, 2016.
doi:10.2528/PIERM15110306

9. Baraniuk, R. G., "Compressive sensing," IEEE Signal Processing Magazine, 118, July 2007.

10. Fornasier, M. and H. Rauhut, Handbook of Mathematical Methods in Imaging, Chapter 6, 187-228, Springer, 2011.

11. Fornasier, M. and H. Rauhut, Handbook of Mathematical Methods in Imaging, Vol. 1, Springer, 2010.

12. Candes, E., J. Romberg, and T. Tao, "Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information," IEEE Transactions on Information Theory, Vol. 52, No. 2, 489-509, February 2006.
doi:10.1109/TIT.2005.862083

13. Romberg, J., , L1qc logbarrier.m.statweb.stanford.edu/˜candes/l1magic/, October 2005, accessed: 2016-11-15.

14. ANSYS HFSS for Antenna Design Training Manual, 1st Ed., ANSYS, May 2016.

15. Fang, H., S. A. Vorobyov, H. Jiang, and O. Taheri, "Permutation meets parallel compressed sensing: How to relax restricted isometry property for 2D sparse signals," IEEE Transactions on Signal Processing, Vol. 62, No. 1, 196-210, January 2014.
doi:10.1109/TSP.2013.2284762

16. Boufonos, P., M. F. Duarte, and R. G. Baraniuk, "Sparse signal reconstruction from noisy compressive measurement using cross validation," 2007 IEEE/SP 14th Workshop on Statistical Signal Processing, IEEE Signal Processing Society, IEEE, August 2007.