Radio Physics and Radio Astronomy (Sep 2023)
A STUDY OF MATERIAL RECOGNITION ACCURACY BY RADIO WAVE METHODS
Abstract
Subject and Purpose. Accuracy of the material recognition using radio wave methods in the ultra-high frequency band for substanc- es with dielectric properties is the present paper concern. To estimate a total error arising in material measurements by the radio wave method and determine constituents of the error is the aim of the work. Methods and Methodology. The material recognition accuracy is estimated by the method of statistical analysis in terms of average statistical deviation and dispersion. Results. Advantages and disadvantages of the waveguide method in which a test material sample is placed inside a metal waveguide have been traced, suggesting an obvious drawback that the material recognition process of the sort is difficult to be automated. In the remote inspection procedure, the test material is in free space (e.g. on a conveyor) where it is illuminated with a microwave transmitting antenna. The receiving antenna is located on the other side of the test sample and transfers the received signal to the apparatus for determining material parameters. There, the attenuation coefficient is measured as the electromagnetic wave passes through the sample. The measurement results show a correlation dependence of the wave attenuation coefficient on the quality of the substance, enabling us to use frequency dependences of the material to reveal its unknown quality. The remote method makes it possible to automate the recognition of materials with dielectric properties. For these methods mentioned right above, random error values arising during the material recognition were estimated by the method of statistical analysis. Conclusions. The analysis of errors in the material recognition shows that the total error ranges from 7.28 to 12.74% with corresponding constituent errors including faults of today’s microwave measuring devices, inappropriate application of the method or unsuitable type of the structural model of the parameter determination, and errors in data calculations.
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