UTC 2022 Funding - Cycle 2 Research Projects

Project Number: CY2-OU-15
Project Title: Surface Penetration and Imaging for Infrastructure Inspection Using Radar Sensors as UAS Payload – Year Two Effort
Performing Institution: University of Oklahoma
Principal Investigator: Yan (Rockee) Zhang and Hernan Suarez, School of Electrical and Computer Engineering and Advanced Radar Research Center, University of Oklahoma
Proposed Start and End Date: 10/01/2024 to 09/30/2025
Project Description: Even though radar technologies, such as Ground Penetration Radar (GPR) and Synthetic Aperture Radar (SAR), have been investigated in previous USDOT projects based on the advantages of all-weather and surface penetration sensing capabilities, a small, agile, and low-power version of such radar as a payload of Unmanned Aerial Vehicle (UAV) or Unmanned Ground Vehicles (UGV) have not yet been demonstrated before. Another trend in bridge/road/pavement inspection is robot-based, automatic, multi-sensor integration from a distributed network. Several manned ground-based platforms with cameras and other sensors (LIDAR, acoustic, piezoelectric, IR, RFID, etc.) have been reported to be used previously in DOT projects. This includes machine learning (ML) processing methods to identify structure and material health issues better. However, more in-depth investigation is still needed to mature these algorithms. Transferring these R&D efforts to operational capabilities still depends on real-world challenges, such as infrastructure accessibility, safety, environments, and maturity of the sensor systems.

The novel contribution of the new radar sensor package proposed from this project mainly lies in three aspects: (1) Wideband microwave radar inspection with both designs from lower-frequency, traditional GPR frequency band, and higher frequency, microwave radar band that offers better resolution and smaller sensor aperture sizes, by leveraging the latest component technology of radar sensors. (2) Enabling and implementing the integration into a small UAS (sUAS) platform, which has fewer restrictions from ground traffic, can access the difficult areas for human operators and demonstrate such platforms through flight tests. (3) Introduction of Machine Learning (ML) method based on high-fidelity physical modeling of the interactions between structures and microwave sensors and decision-tree type sensor data models. Thus, the capability of detecting various types of defects in the 3D domain is enhanced compared to existing radar sensors.
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