Vegetation image as Bayesian predictor for radio propagation in complex environments using unscented transform

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.