The detection of Foreign Object Debris (FOD) is crucial for maintaining safety in critical areas like airport runways. This paper presents a comprehensive review of FOD detection technologies, covering traditional, radar-based, and artificial intelligence (AI)-driven methods. Manual visual inspection and optical sensors, while widely used, are limited in scalability and reliability under adverse conditions. Radar technologies, such as millimeter-wave radar and synthetic aperture radar, offer robust performance, with advancements in algorithms and sensor fusion significantly enhancing their effectiveness. AI approaches, employing supervised and unsupervised learning, demonstrate potential for automating detection and improving precision, although challenges such as limited datasets and high computational demands persist. This review consolidates the recent progress across these domains, highlighting the need for integrated systems that combine radar and AI to improve adaptability, scalability, and small-FOD detection. By addressing these limitations, the study provides insights into future research directions and the development of innovative FOD detection solutions, contributing to safer and more efficient operational environments.

Radar systems operating aboard unmanned aerial systems (UASs) or drones are gaining lot of interest in the remote sensing field. UASs have recently been tested as platforms for synthetic aperture radar (SAR) systems. There are many expectations regarding the interferometric capabilities of UAS-based radar interferometers. Nevertheless, its implementation introduces new problems, which demand advanced processing techniques. To date, only few works presented interferometric data acquired aboard UAS. In this paper repeat-pass interferometric tests are presented, performed with an S-band UAS-based SAR. By using corner reflectors subject to known displacements, the interferometric capabilities of the sensor are demonstrated. Results from two measurement campaigns are presented: data acquired in controlled scenario and in a vegetated slope. A focusing algorithm that considers the UAS position and attitude information, able to cope with the UAS instabilities, is presented and discussed. At the knowledge of the authors, the experimental results obtained are the first systematic demonstration of repeat-pass UAS-based SAR interferometry, validated by ground truth.

Interferometric radars are widely used sensors for structural health monitoring. They are able to perform dynamic measurements of displacement with sub-millimeter precision. Today, the Ku-band is the most common, due to the spread of commercial systems operating in this band. At the same time, the W-band sensors are gaining ever more interest. Other popular systems work in the X-band. Since the characteristics of the measurements dramatically depend on the operative frequency, it is essential to highlight their differences. For instance, higher frequency allows for high displacement resolution, but it is more subject to phase wrapping and decorrelation effects. In this paper, a direct comparison between radars operating in X, Ku, and W-band for bridge monitoring is carried out. The radars provide frequency-modulated continuous-wave signals. Experimental campaigns were performed both in controlled and realistic scenarios (a stayed bridge). The results of the experiments demonstrate that all the three sensors are suitable for performing dynamic structure monitoring despite their differences. It is worth noting that this comparative analysis has highlighted the role of amplitude variation in phase/displacement measurement. Regarding this point, the three different bands exhibit significant differences.

In this work, the authors propose a novel electronic architecture named VESTA (Versatile External Synchronization and Telemetry Architecture), capable of independently estimating position and attitude with centimeter-level accuracy, specifically designed to be mounted as a payload for multicopter drones. VESTA integrates GPS-RTK and inertial-sensing technologies to generate a high-precision telemetry data stream, which is transmitted via a dedicated UART (Universal Asynchronous Receiver-Transmitter) interface. The key innovation of VESTA lies in its robust temporal synchronization mechanism: by exploiting GPS time signals, including UTC timestamps and PPS (Pulse Per Second) signals, each telemetry sample is uniquely timestamped with millisecond resolution. To the best of our knowledge, current commercial and custom drone navigation systems lack a reliable method for achieving this level of absolute time synchronization between inertial and GNSS data. This limitation hinders precise data fusion and complicates integration with other systems requiring temporally aligned information. VESTA addresses this gap, enabling accurate and unambiguous time correlation across subsystems. The positioning accuracy and synchronization performance of VESTA were validated through comparative testing with the DJI Matrice 300 RTK, one of the most advanced commercial UAS platforms, which notably does not support GPS-based timestamping. A Leica total station, providing millimeter-level ground truth, was employed to track the drone’s trajectory and quantitatively assess the estimation errors of both systems.

Radar interferometry can provide important information for Structural Health Monitoring (SHM) of bridges and other transportation structures. In this article, joint communication and sensing (JCAS) telecommunication infrastructure is tested as a ground-based radar, offering advantages in terms of long-term costs, deployment and maintenance. This work specifically addresses the estimation of the radar support movement (i.e., pylon or mast), which represents a major challenge in this kind of measurements. Movements of the radar system combine with the true target motion and, if not correctly compensated, can compromise the accuracy of the results. A technique for estimating radar movements based on the displacement tracking of multiple permanent scatterers (PSs) in the scenario is presented. True target displacements can then be retrieved by applying linear regression methods to fixed PSs located at different viewing angles, accounting for both radar movements and atmospheric displacement components. The technique was validated using real data acquired during an experimental campaign on a bridge test site. First, results obtained for a target subject to known displacements are shown. A second measurement session was aimed at testing the method for bridge dynamic monitoring. Finally, the same technique was applied antenna mast monitoring in terms of modal analysis and vibration characterization.

This paper presents a seismic accelerometer with GPS-synchronization based on the pulse per second (PPS) signal provided by the GNSS satellite network. This feature is essential for a Structural Health Monitoring (SHM) network that includes different sensors as for example accelerometers, cameras and a ground-based radar. The PPS signal is widely used in applications of timing synchronization because of its high accuracy and long-term stability. The common time-base between devices allows the internal time of the devices to be resynchronized every second, avoiding drift. In this work, we developed and tested acquisition board for seismic accelerometer with custom firmware designed to provide synchronized data recorded by different acquisition systems. The universal time (UTC) synchronized with PPS has been assigned to the data of each devices. The developed board has been tested with simultaneous acquisition a seismic accelerometer in MEMS (Micro Electro-Mechanical System) technology and a ground-based interferometric radar,both GPS synchronized. The two sensors are equipped with high precision GNSS receiver u-blox F9, that provide the UTC data and the PPS signal. To validate the synchronization system, the accelerometer and the ground-based radar were operated simultaneously to detect the oscillations of a moving target (a metallic swinging rod). The results of the experiment demonstrate that the sensors are well synchronized and the evaluations of the displacement are very similar, with good accuracy for performing dynamic structure monitoring. It is worth noting that this comparative analysis has highlighted the role of the automatic synchronization.