Real‐time object detection using power spectral density of ground‐penetrating radar data

Abstract

A statistical analytical monitoring scheme is developed that utilizes maximum energy of ground-penetrating radar signals to detect hidden buried objects and estimate their location and depth automatically. The maximum energy is calculated for locations by Welch's power spectral density estimation. Using the proposed analytic, the maximum energy is tightly monitored for a significant change from reference signals generated using target-free locations. A warning message is triggered when monitoring process detects a site with potential buried objects, on average, 90 cm (2.95 ft) away from the object for 800-MHz antenna. Continuing the ground-penetrating radar scan in the same direction and monitoring the signals, the procedure uses a sophisticated hyperbola-mapping method to estimate the location and depth of buried objects with high accuracy. The analytics could successfully pinpoint the location and depth of hidden objects, respectively, with mean absolute error of 0.38 and 2.03 cm in synthetic noisy environments. Reliable performance of the proposed analytics in real cases that run in real-time for multiple object detection even in noisy media proves its efficiency for real-life exploration.