VEGETATION IMAGE AS BAYESIAN PREDICTOR FOR RADIO PROPAGATION IN COMPLEX ENVIRONMENTS USING UNSCENTED TRANSFORM

Authors

  • Alexandre J. F. Loureiro
  • Leonardo R.A.X. Menezes
  • Glaucio L. Ramos
  • Paulo T. Pereira
  • Mateus H. B. Rezende

DOI:

https://doi.org/10.1590/2179-10742018v17i21260

Keywords:

Bayes Theorem, Centimeter Wave, Unscented Transform, Vegetation Propagation measurements

Abstract

Vegetation is considered a complex environment for analysis of scattering and attenuation in radio propagation phenomena. Satellite image processing can improve planning of radio systems with a vegetation attenuation predictor. In this research, the prediction is based on the correlation of more than 56% between RGB pixel values and vegetation attenuation taken from three groups of power measurements at two distinct regions of Brazil: Belo Horizonte, in the southeast region measured at 18 GHz, and Manaus at 24 GHz in the north region. The statistical analysis showed that more than 30% of the attenuation variance was due to the pixel values for each group. Using this linear correlated model between vegetation pixel RGB values and geolocated attenuation values, this work combined the unscented transform (UT) and Bayesian inference to refine the vegetation attenuation distribution. Since the necessary multiplication of Bayes prior and sampling distributions is not easily available in the UT, this paper presents a method that calculates new common sigma points and different new weights for the prior and sampling UT distributions, thus allowing the multiplication and creating the basis for a machine learning predictor tool.

References

[1] J. Richter, R.F.S. Caldeirinha, Al-Nuaimi, et al., “A generic narrowband model for radiowave propagation through
vegetation”, VTC IEEE, Stockholm, Sweden, 2005, DOI: 10.1109/VETECS.2005.1543245
[2] David. L. Jones et al., “Vegetation Loss Measurements at 9.6, 28.8, 57.6, and 96.1 GHz Through a Conifer Orchard in
Washington State”, U.S. Department of Commerce, NTIA Report 89-251, October 1989.
[3] N.C. Rogers et al, “A generic model of 1-60 GHz radio propagation through vegetation-final report”, Radio Comm.
Agency, May 2002.
[4] L.R.A.X. Menezes, A. Ajayi, C. Christopoulos, et al., “Efficient computation of stochastic electromagnetic problems
using unscented transforms”, IET Science, Measurement & Technology, 2008, 2, 2, p. 88-95, DOI: 10.1049/ietsmt:20070050
[5] A. Gelman, J. B. Carlin, H. S. Stern, and D. B. Rubin, et al., “Bayesian Data Analysis”, 3rd ed., Chapman and Hall
Book, 2003. ISBN: 9781439898208
[6] T. S. Rappaport et al., “Wideband Millimeter-Wave Propagation Measurements and Channel Models for Future
Wireless Communication System Design”, IEEE Transactions on Comm., vol. 63, nº 9, Sep 2015.
[7] P. Mogensen et al., “Centimeter-Wave Concept for 5G Ultra-Dense Small Cells”, IEEE Vehicular Technology
Conference (VTC), May 2014.
[8] I. Rodriguez et al., “24 GHz cmWave Radio Propagation Through Vegetation: Suburban Tree Clutter Attenuation”,
European Conference on Antennas and Propagation (EuCAP), May 2016.
[9] I. Rodriguez et al., “Analysis and Comparison of 24 GHz cmWave Radio Propagation in Urban and Suburban
Scenarios”, IEEE Wireless Communications and Networking Conference, April 2016.
[10] M. H. B. Rezende, G. L. Ramos, et al., “18 GHz Propagation Measurements and Analysis in Belo Horizonte/Brazil”,
IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications (APWC 2017), Verona,
Italy, 2017, DOI: 10.1109/APWC.2017.8062251
[11] T. S. Rappaport et al., “Millimeter Wave Wireless Communications”, 1st Edition, Prentice Hall, 2015.
[12] W. Song, X. Mu, G. Yan, S. Huang, “Extracting the Green Fractional Vegetation Cover from Digital Images Using a
Shadow-Resistant Algorithm (SHAR-LABFVC)”, Remote Sensing, 2015, 7, p. 10425-10443, DOI:
10.3390/rs70810425
[13] Jiapaer, G.; Chen, X.; Bao, A. A comparison of methods for estimating fractional vegetation cover in arid regions.
Agric. Forest Meteorol. 2011, 151, 1698–1710.
[14] Fernandes, L.C., Menezes, L.R.A.X., “Using Unscented Transform in Interference Studies”, IEEE Antennas and
Propagation Magazine, 2017
[15] S. J. Julier, J. K. Uhlmann, and H. F. Durrant-Whyte, "A new approach for filtering nonlinear systems", Proc. Amer.
Contr. Conf., pp. 1628-1632, 1995
[16] L.R.A.X. Menezes, A.J.F. Loureiro, “cmWave through vegetation: correlation of pixels and attenuation using UT and
Bayes Inference”, IEEE Antennas & Propagation Society Symposium. APSURSI, San Diego, USA, 2017. DOI:
10.1109/APUSNCURSINRSM.2017.8072957

Downloads

Published

2018-09-30

How to Cite

Alexandre J. F. Loureiro, Leonardo R.A.X. Menezes, Glaucio L. Ramos, Paulo T. Pereira, & Mateus H. B. Rezende. (2018). VEGETATION IMAGE AS BAYESIAN PREDICTOR FOR RADIO PROPAGATION IN COMPLEX ENVIRONMENTS USING UNSCENTED TRANSFORM. Journal of Microwaves, Optoelectronics and Electromagnetic Applications (JMOe), 17(2), 284-297. https://doi.org/10.1590/2179-10742018v17i21260

Issue

Section

Regular Papers