Distance Measurement by Neural Network Learning of Near-Field Microwave Reflection Spectra

Microwave-based distance measurements are limited depending on the sensing environment, such as the propagation medium and surrounding obstacles, and the complex environment also affects the measurement performance. To tackle this problem, we propose a method for predicting the distance based on the...

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Bibliographic Details
Published in:Journal of sensors 2022-09, Vol.2022, p.1-9
Main Authors: Jeong, Janghoon, Son, Seong-Ho
Format: Article
Language:English
Subjects:
Artificial neural networks
Cameras
Distance measurement
Learning
Machine learning
Near fields
Neural networks
Sensors
Spectra
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Summary:Microwave-based distance measurements are limited depending on the sensing environment, such as the propagation medium and surrounding obstacles, and the complex environment also affects the measurement performance. To tackle this problem, we propose a method for predicting the distance based on the artificial neural network learning of near-field microwave reflection spectra. In principle, the spectral data is expected to contain a signature of the distance of the target object. Based on this, we proposed a two-step neural network to extend the measurable distance range while ensuring prediction performance. The first step is to predict the coarse range of the target by classification, and the next is to predict the precise distance value through multidimensional regression within that coarse range. The method was verified through experiments to predict the position of an object in an underwater environment, which was difficult to measure with conventional methods.
ISSN:1687-725X
1687-7268
DOI:10.1155/2022/1425952