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

초록

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.

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