Dextractor:Deformation Extractor Framework for Monitoring-Based Ground Radar

Abstract

The radio frequency (RF) data generated from a single-chip millimeter-wave (mmWave) ground-based multi-input multi-output (GB-MIMO) radar can provide a highly robust, precise measurement for deformation in harsh environments, overcoming challenges such as different lighting and weather conditions. Monitoring deformation is significant for safety factors in different applications, such as detecting and monitoring the ground stability of underground mines. However, radar images can experience different types of clutter and artifacts besides the spreading effects caused by the side lobes, resulting in the foremost challenge of suppressing clutter and monitoring deformation.In the state of the art, the introduced frameworks usually include many filters proposed for different types of noise, with commercial systems typically using an amplitude threshold. This paper proposes a framework for monitoring the deformation, where the essential process is to apply a data-driven threshold to the amplitude heatmap, detect the deformation, and eliminate noise. The proposed threshold is an iterative approach based on radar imagery statistics, and it performs well for the collected dataset. The principal advantage of our proposed framework is simplicity, reducing the burden of using different filters. We can consider the dynamic threshold based on data statistics as a data-driven machine learning tool. The results show promising performance for our method in monitoring the deformation and removing clutter compared to the benchmark method.