[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123176-en":3,"doc-seo-123176-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123176,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Signal Detection with a Digital Antenna Array using Machine Learning - Image Recognition Methods Applied for Joint Detection and Signal Parameter Estimation","Radars and radar warning receiver systems operate in a continuous contest for superior detection performance, motivating new approaches. This master’s thesis designs a “You Only Look Once”-style neural network to detect pulsed radar signals and estimate angle of arrival, frequency, time of arrival, and pulse width. A digital uniform linear antenna array provides multi-channel outputs, processed with short-time Fourier transforms and beamforming across angles to form a 3D spectrogram image for recognition. Simulated results demonstrate detection of two sinusoidal signals in noisy environments.","Signal Detection with a Digital Antenna Array using Machine Learning  \nImage Recognition Methods Applied for Joint Detection and Signal Parameter Estimation  \nMaster’s thesis in Physics  \nElin Ohlman  \nDEPARTMENT OF ELECTRICAL ENGINEERING  \nCHALMERS UNIVERSITY OF TECHNOLOGY Gothenburg, Sweden 2023  \n[www.chalmers.se](www.chalmers.se)  \nMaster’s thesis 2023  \nSignal Detection with a Digital Antenna Array using Machine Learning  \nImage Recognition Methods Applied for Joint Detection and Signal  \nParameter Estimation  \nELIN OHLMAN  \nDepartment of Electrical Engineering Division of Communications, Antennas, and Optical Networks Chalmers University of Technology Gothenburg, Sweden 2023  \nSignal Detection with a Digital Antenna Array using Machine Learning  \nImage Recognition Methods Applied for Joint Detection and Signal Parameter Estimation  \nELIN OHLMAN  \n© ELIN OHLMAN, 2023 .  \nSupervisor: Eric Norgren, SAAB  \nSupervisor: Alexander Karlsson, SAAB  \nExaminer: Rob Maaskant, Department of Electrical Engineering, Chalmers  \nMaster’s Thesis 2023  \nDepartment of Electrical Engineering  \nDivision of Communications, Antennas, and Optical Networks Chalmers University of Technology  \nSE-412 96 Gothenburg Telephone +46 31 772 1000  \nCover: Predictions and targets for a 3D image with the angle of arrival, frequency, and time as the three axes. The predictions are made by the network developed in the thesis.  \nTypeset in LATEX  \nGothenburg, Sweden 2023  \nSignal Detection with a Digital Antenna Array using Machine Learning  \nImage Recognition Methods Applied for Joint Detection and Signal Parameter Estimation  \nELIN OHLMAN  \nDepartment of Electrical Engineering Chalmers University of Technology  \nAbstract  \nRadars and radar warning receiver systems engage in a perpetual battle for superiority. This constant struggle demands the development of new methods, and recent advancements in machine learning and image recognition may serve as sources of inspiration. In this thesis, a “You Only Look Once”-style neural network is designed to detect pulsed radar signals and estimate their angle of arrival, frequency, time of arrival, and pulse width. Employing a digital multi-channel uniform linear antenna array, a short-time Fourier transform is applied to the output from each antenna element. Afterward, beamforming is performed for various angles combining the spectrograms, producing a 3D image upon which image recognition is applied. We find that using simulated data this method works for detecting two sinusoidal signals, even in noisy environments.  \nKeywords: Angle of Arrival (AOA), Beamforming, Convolutional Neural Network (CNN), Digital Antenna, Signal Detection, Spectrogram, Uniform Linear Array (ULA), You Only Look Once (YOLO)  \nAcknowledgements  \nFirst I would like to thank my supervisors at Saab, Eric Norgren, and Alexander Karlsson for the opportunity to do this thesis work. This project would never have been possible without your support, patience, and guidance. To my academic supervisor at Chalmers, Rob Maaskant, thank you for your support and understanding. To my partner Lukas Falk, thank you for your love and unwavering faith in me, even when I myself lacked it. Lastly, I thank my family, Börje, Hanna, Elsa, and Erik Ohlman for your everlasting support and encouragement.  \nElin Ohlman, Stockholm, May 2023  \nList of Acronyms  \nBelow is the list of acronyms that have been used throughout this thesis listed in alphabetical order.  \nAOA Angle of Arrival  \nCNN Convolutional Neural Network  \nFFT Fast Fourier Transform  \nIoU Intersection over Union  \nIQ In-phase and Quadrature  \nML Machine Learning  \nNN Neural Network  \nPW Pulse Width  \nResNet Residual Neural Network  \nRF Radio Frequency  \nRMSE Root Mean Square Error  \nSNR Signal to Noise Ratio  \nSTFT Short Time Fourier Transform  \nTOA Time Of Arrival  \nULA Uniform Linear Array  \nYOLO You Only Look Once","cbCaijFASvsgV4RN","https://ap.wps.com/l/cbCaijFASvsgV4RN","pdf",2866288,1,56,"English","en",105,"# Abstract\n# Keywords\n# Acknowledgements\n# List of Acronyms","[{\"question\":\"What signals and parameters does the thesis focus on detecting and estimating?\",\"answer\":\"It targets pulsed radar signals and estimates angle of arrival, frequency, time of arrival, and pulse width.\"},{\"question\":\"How is the antenna array data processed before neural network-based recognition?\",\"answer\":\"Each antenna element output is transformed using a short-time Fourier transform, followed by beamforming across angles to create a 3D image used for recognition.\"},{\"question\":\"What neural network approach is used, and what does the thesis report about its performance?\",\"answer\":\"A “You Only Look Once”-style neural network is used; with simulated data it can detect two sinusoidal signals even in noisy conditions.\"}]","Signal Detection with a Digital Antenna Array using Machine Learning - Image Recognition Methods Applied for Joint Detection and Signal Parameter Estimation | PDF",1785815040,141,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"signal-detection-with-a-digital-antenna-array-using-machine-learning-image-recognition-methods-applied-for-joint-detection-and-signal-parameter-estimation","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/signal-detection-with-a-digital-antenna-array-using-machine-learning-image-recognition-methods-applied-for-joint-detection-and-signal-parameter-estimation/123176/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What signals and parameters does the thesis focus on detecting and estimating?","Question",{"text":75,"@type":76},"It targets pulsed radar signals and estimates angle of arrival, frequency, time of arrival, and pulse width.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the antenna array data processed before neural network-based recognition?",{"text":80,"@type":76},"Each antenna element output is transformed using a short-time Fourier transform, followed by beamforming across angles to create a 3D image used for recognition.",{"name":82,"@type":73,"acceptedAnswer":83},"What neural network approach is used, and what does the thesis report about its performance?",{"text":84,"@type":76},"A “You Only Look Once”-style neural network is used; 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