This paper investigates the use of ground-penetrating radar (GPR) technology for detecting shallow buried objects, utilizing an air-coupled stepped frequency continuous wave (SFCW) radar system that operates within a 2 GHz bandwidth starting at 500 MHz. Different GPR data acquisition methods for air-coupled systems are compared, specifically down-looking, side-looking, and circular acquisition strategies, employing the back projection algorithm to provide focusing of the acquired GPR data. Experimental results showed that the GPR can penetrate up to 0.6 m below the surface in a down-looking mode. The developed radar and the back projection focusing algorithm were used to acquire data in the side-looking and circular mode, providing focused images with a resolution of 0.1 m and detecting subsurface objects up to 0.3 m below the surface. The proposed approach transforms B-scans of the GPR-based data into 2D images. The provided approach has significant potential for advancing shallow object detection capabilities by transforming hyperbola-based features into point-like features.

Air-coupled ground penetrating radar (GPR) systems are widely used for subsurface imaging in demining, geological surveys, and infrastructure assessment applications. However, strong surface reflections can introduce interference, leading to receiver saturation and reducing the clarity of subsurface features. This paper presents a novel surface reflection suppression algorithm for stepped-frequency continuous wave (SFCW) GPR systems. The proposed method estimates the surface reflection component and applies phase-compensated subtraction at the receiver site, effectively suppressing background reflections. A modular SFCW radar system was developed and tested in a laboratory setup simulating a low-altitude airborne deployment to validate the proposed approach. B-scan and time-domain analyses demonstrate significant suppression of surface reflections, improving the visibility of subsurface targets. Unlike previous static echo cancellation methods, the proposed method performs on-board pre-downconversion removal of surface clutter that compensates for varying ground distance, which is a unique contribution of this work.

V prispevku je predstavljen realnočasovni sistem za zaznavo brezpilotnih letalnikov, ki združuje vizualno detekcijo na osnovi globokega učenja in napredno radarsko obdelavo Range–Doppler signalov FMCW. Vizualni del temelji na modelu YOLOv8, implementiranem na platformi NVIDIA Jetson Orin Nano z uporabo industrijske kamere Basler. Radarski podsistem, izveden na Raspberry Pi, vključuje Gaussovo glajenje, odštevanje ozadja ter detekcijo ciljev z uporabo pragovne metode in gručenja DBSCAN v prostoru razdalje. Med podsistemoma je vzpostavljena komunikacija UDP, ki omogoča časovno usklajevanje zaznav in njihovo združevanje znotraj definiranega časovnega intervala. Eksperimentalni rezultati potrjujejo, da kombinirani pristop izboljša zanesljivost zaznave v primerjavi s posameznimi metodami, zlasti v zahtevnih okoljskih pogojih. Prispevek analizira arhitekturo sistema, zmogljivost izvajanja na robnih napravah ter omejitve in možnosti nadaljnjih izboljšav.