1. Uporaba algoritmov globokega učenja za zaznavo brezpilotnih letalnikovPane Andov, 2025, master's thesis Abstract: V magistrskem delu je bila predstavljena metoda za zaznavanje brezpilotnih letalnikov, ki združuje optično kamero in radar. V optičnem podsistemu so bili objekti zaznavani z uporabo algoritma YOLOv8, implementiranega na energijsko učinkoviti platformi NVIDIA Jetson Orin Nano, ki omogoča obdelavo v realnem času. Radarski podsistem temelji na FMCW radarju, ki izkorišča Dopplerjev pojav pri frekvenci 10 GHz in pasovni širini 500 MHz, kar omogoča natančno merjenje razdalje in hitrosti. Eksperimentalni rezultati so pokazali, da združevanje optičnih in radarskih podatkov poveča robustnost in zanesljivost zaznavanja brezpilotnih letalnikov v različnih okolijskih pogojih.
Keywords: dron, detekcija dronov, radar, Dopplerjev radar, YOLOv8
Published in DKUM: 04.09.2025; Views: 0; Downloads: 26
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2. Zaznavanje objektov pod površjem zemlje z uporabo bistatičnega radarja FMCW : magistrsko deloKlemen Kropec, 2025, master's thesis Abstract: V magistrski nalogi je predstavljeno delovanje FMCW-radarja, eksperimentalna evalvacija radarja ter sinhronizacija FMCW-radarja v bistatični konfiguraciji. FMCW-radar smo najprej pripravili v monostatični strukturi ter z njim opravili eksperimentalne preizkuse, njihove vrednosti primerjali s teoretičnimi izračuni ter na podlagi tega ocenili primernost sistema za nadaljnje preizkuse. Nato smo opravili sinhronizacijo v bistatični konfiguraciji. Najprej smo opravili simulacije, nato pa preizkuse izvedli še praktično. Sinhronizacijo smo izvedli na dva načina: najprej z uporabo reference iz lokalnega oscilatorja, nato pa še z uporabo reference oddajnika. Izvedli smo preizkuse na testnem poligonu in evalvacijo v bistatični strukturi.
Keywords: FMCW-radar, sinhronizacija bistatičnega radarja, evalvacija FMCW-radarja
Published in DKUM: 03.06.2025; Views: 0; Downloads: 48
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3. Surface Reflection Suppression Method for Air-Coupled SFCW GPR SystemsPrimož Smogavec, Dušan Gleich, 2025, original scientific article Abstract: 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.
Keywords: GPR systems, ground penetrating radar systems, surface reflection suppression, SFCW, surface echo, echo cancellation, receiver saturation
Published in DKUM: 12.05.2025; Views: 0; Downloads: 14
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4. Comparative Study of GPR Acquisition Methods for Shallow Buried Object DetectionPrimož Smogavec, Blaž Pongrac, Andrej Sarjaš, Venceslav Kafedziski, Nabojša Dončov, Dušan Gleich, 2024, original scientific article Abstract: 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.
Keywords: GPR, UAV, SFCW radar, acquisition methods
Published in DKUM: 04.12.2024; Views: 0; Downloads: 18
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7. Cross-Hole GPR for Soil Moisture Estimation Using Deep LearningBlaž Pongrac, Dušan Gleich, Marko Malajner, Andrej Sarjaš, 2023, original scientific article Abstract: This paper presents the design of a high-voltage pulse-based radar and a supervised data processing method for soil moisture estimation. The goal of this research was to design a pulse-based radar to detect changes in soil moisture using a cross-hole approach. The pulse-based radar with three transmitting antennas was placed into a 12 m deep hole, and a receiver with three receive antennas was placed into a different hole separated by 100 m from the transmitter. The pulse generator was based on a Marx generator with an LC filter, and for the receiver, the high-frequency data acquisition card was used, which can acquire signals using 3 Gigabytes per second. Used borehole antennas were designed to operate in the wide frequency band to ensure signal propagation through the soil. A deep regression convolutional network is proposed in this paper to estimate volumetric soil moisture using time-sampled signals. A regression convolutional network is extended to three dimensions to model changes in wave propagation between the transmitted and received signals. The training dataset was acquired during the period of 73 days of acquisition between two boreholes separated by 100 m. The soil moisture measurements were acquired at three points 25 m apart to provide ground truth data. Additionally, water was poured into several specially prepared boreholes between transmitter and receiver antennas to acquire additional dataset for training, validation, and testing of convolutional neural networks. Experimental results showed that the proposed system is able to detect changes in the volumetric soil moisture using Tx and Rx antennas.
Keywords: ground penetrating radar, cross-hole, L-band, deep learning, convolutional neural network, soil moisture estimation
Published in DKUM: 03.04.2024; Views: 448; Downloads: 33
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8. Despeckling of SAR Images Using Residual Twin CNN and Multi-Resolution Attention MechanismBlaž Pongrac, Dušan Gleich, 2023, original scientific article Abstract: The despeckling of synthetic aperture radar images using two different convolutional neural network architectures is presented in this paper. The first method presents a novel Siamese convolutional neural network with a dilated convolutional network in each branch. Recently, attention mechanisms have been introduced to convolutional networks to better model and recognize features. Therefore, we propose a novel design for a convolutional neural network using an attention mechanism for an encoder–decoder-type network. The framework consists of a multiscale spatial attention network to improve the modeling of semantic information at different spatial levels and an additional attention mechanism to optimize feature propagation. Both proposed methods are different in design but they provide comparable despeckling results in subjective and objective measurements in terms of correlated speckle noise. The experimental results are evaluated on both synthetically generated speckled images and real SAR images. The methods proposed in this paper are able to despeckle SAR images and preserve SAR features.
Keywords: synthetic aperture radar, speckle, speckle suppression, despeckling, deep learning, convolutional neural network
Published in DKUM: 21.02.2024; Views: 280; Downloads: 37
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9. Odstranjevanje pegastega šuma iz slik SAR z uporabo globokega učenja : magistrsko deloTadej Habjanič, 2023, master's thesis Abstract: Postopek odstranjevanja pegastega šuma je neizogiben pri obdelavi slik z radarjem s sintetično odprtino (SAR). Obstaja več različnih metod za odstranjevanje pegastega šuma, vendar se je postopek s konvolucijsko nevronsko mrežo (CNN) izkazal kot zelo učinkovita metoda.
Pri preprosti strukturi CNN se še vedno izgubi precejšnje število podrobnosti na sliki. Za rešitev tega problema je bila uporabljena arhitektura kodirnika – dekoderja. Model se uči s pristopom, ki temelji na veliki količini podatkov, z uporabo algoritma gradientnega spuščanja s kombinacijo spreminjanja ojačanja pri odstranjevanju šuma in funkcije izgube celotne variacije. Poskusi, izvedeni na realnih slikah, kažejo, da ta metoda dosega pomembne izboljšave v primerjavi z ostalimi metodami.
Keywords: pegasti šum, radar s sintetično odprtino, konvolucijska nevronska mreža, arhitektura kodirnik – dekodirnik
Published in DKUM: 06.02.2024; Views: 274; Downloads: 48
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10. Development of an automated measurement system for UWB Radar : master's thesisTomaž Leopold, 2022, master's thesis Abstract: Ultra-wideband is becoming a key technological element in the consumer industry, providing a safe and reliable way to enable more precise location services as well as secure access to a multitude of systems. Ultra-wideband radar is a subset of that technology, providing not only device-free localization techniques but also complementing expensive medical-level devices or providing means to measure vital signs in a more accessible way. Ultra-wideband radars embedded in smart devices like smartphones can be used for presence detection, gesture recognition, and vital sign monitoring (i.e., breathing detection and heart rate monitoring). Ultra-wideband radar is still in development, and during that process it needs to be well-tested to match the customers’ expectations and standards that apply to the technology. In this master’s thesis, we start by providing a brief overview of ultra-wideband technology. Then we propose and develop an automated measurement system for UWB radar that tests the radio frequency functionality of a radar device, its compliance with standards, frequency regulations, and use-case testing. Performing measurements to test the aforementioned radar devices is a time-consuming and cumbersome task that can be replaced by our developed automated measurement system. We demonstrate the advantages in terms of reduced measurement time and improved test reproducibility, resulting in a well-tested ultra-wideband radar device during the development cycle.
Keywords: automated system, channel impulse response, IR-UWB, radar
Published in DKUM: 17.02.2022; Views: 1094; Downloads: 14
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