[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123936-en":3,"doc-seo-123936-105":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},123936,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","First Principles Machine Learning in Radar - Augmenting Signal Processing Techniques with Machine Learning for Detection, Tracking, and Navigation - Dissertation","A dissertation investigates first-principles machine learning methods integrated with radar signal processing to enhance detection, tracking, and navigation. It develops a self-explaining decision architecture featuring data encoding, feature extraction, sequence interpretation, decision generation, and explanation generation, alongside counterfactual drift correction and meta-cognitive radar detection. The work also addresses covariance estimation and causality in machine learning, then applies causal ML to predictive navigation using explicit motion models, simulation design, and experimental evaluation.","UNIVERSITY OF OKLAHOMA  \nGRADUATE COLLEGE  \nFIRST PRINCIPLES MACHINE LEARNING IN RADAR: AUGMENTING SIGNAL PROCESSING TECHNIQUES WITH MACHINE LEARNING FOR DETECTION, TRACKING, AND NAVIGATION  \nA DISSERTATION  \nSUBMITTED TO THE GRADUATE FACULTY in partial fulfillment of the requirements for the Degree of  \nDOCTOR OF PHILOSOPHY  \nBy  \nALEXANDER J. STRINGER  \nNorman, Oklahoma  \nFIRST PRINCIPLES MACHINE LEARNING IN RADAR: AUGMENTING SIGNAL PROCESSING TECHNIQUES WITH MACHINE LEARNING FOR DETECTION, TRACKING, AND NAVIGATION  \nA DISSERTATION APPROVED FOR THE  \nSCHOOL OF ELECTRICAL AND COMPUTER ENGINEERING  \nBY THE COMMITTEE CONSISTING OF  \nDr. Justin Metcalf, Chair  \nDr. Andrew Fagg  \nDr. Mark Yeary  \nDr. Nathan Goodman  \nDr. Dean Hougen  \n© Copyright by ALEXANDER J. STRINGER 2024 All Rights Reserved.  \nTo Eileen and Zoey  \niv  \nAcknowledgments  \nI would like to start by thanking my wonderful partner, Eileen. She has been my closest friend and confidant for 19 years, and her encouragement has helped me to stay grounded and upbeat throughout my graduate studies. I would also like to thank my advisor, Dr. Justin Metcalf, for guiding me through my doctoral journey. I find his dedication to his students and passion for this field inspiring, and hope to carry some of that forward throughout my carrier. I would like to thank my committee, Dr. Yeary, Dr. Goodman, and Dr. Hougen, for sharing some of their wisdom with me. Their insights were invaluable to the progression of this work. I would also like to thank the US AirForce’s 76 SWEG and the AirForce Office of Scientific Research (AFOSR) for funding this research. I would especially like to thank my AFOSR sponsor, Dr. Erik Blasch. A huge thank you to my fellow researcher and partner in (figurative) crime, Geoff. He has worked closely with me on much of this research and made my time as a doctoral candidate fun. Another huge thank you to my research team, especially Joe and Timmy. We’ve accomplished a lot, and I’m excited to see where we go from here. I would like to thank my 76 SWEG research director, Adam Bowersox, for the leadership and support he provided to our team. He helped establish an open and collaborative environment for our team. Finally, I would like to thank my mother, Dr. Gillian Bond. She has always been my role model and one of my biggest supporters. Her intellectual curiosity, drive, and integrity are the standards I strive to uphold.  \nTable of Contents  \nList of Tables xii  \nList of Figures xiv  \nAbstract xxi  \n1 Introduction 1  \n1.1 Summary of Contributions ....................... 4  \n1.1.1 The Self-Explaining Decision Architecture .......... 4  \n1.1.2 Counterfactual Drift Correction ................ 6  \n1.1.3 Meta-Cognitive Radar Detection ................ 6  \n1.1.4 Covariance Estimation ..................... 8  \n2 Machine Learning Concepts 9  \n2.1 Introduction ............................... 9  \n2.2 Supervised Learning .......................... 12  \n2.2.1 Decision Trees and Random Forests .............. 12  \n2.2.2 Neural Networks ........................ 16  \n2.2.3 Activation Functions ...................... 31  \n2.2.4 Loss Functions ......................... 32  \n2.3 Machine Learning Attention and the Transformer ........... 34  \n2.4 Generative Machine Learning ..................... 42  \n2.5 Machine Learning Interpretability and Explainability ......... 46  \n2.5.1 Convolutional Batch Attention Module ............ 47  \n2.6 Causality and ML ............................ 49  \n2.7 Genetic Algorithms ........................... 52  \n2.7.1 Multi-Objective Genetic Algorithms .............. 52  \n3 Radar Background 55  \n3.1 Hypothesis Testing and Neyman-Pearson Lemma ........... 55  \n3.2 The Radar Detection Problem Space .................. 57  \n3.3 Interference Models for Adaptive Detection .............. 59  \n3.4 Adaptive Detectors ........................... 65  \n3.4.1 Generalized Likelihood Ratio Test ............... 65  \n3.4.2 Adaptive Matched Filter .................... ","cbCaioIXSLw3JOjM","https://ap.wps.com/l/cbCaioIXSLw3JOjM","pdf",11800629,1,346,"English","en",105,"# Introduction\n## Summary of Contributions\n## Self-Explaining Decision Architecture\n## Counterfactual Drift Correction\n## Meta-Cognitive Radar Detection\n## Covariance Estimation\n# Machine Learning Concepts\n## Supervised Learning\n## Machine Learning Attention and the Transformer\n## Generative Machine Learning\n## Machine Learning Interpretability and Explainability\n## Causality and ML\n## Genetic Algorithms\n# Radar Background\n## Hypothesis Testing and Neyman-Pearson Lemma\n## The Radar Detection Problem Space\n## Interference Models for Adaptive Detection\n## Adaptive Detectors\n## Covariance Modeling and Estimation\n## The Kalman Filter\n# The Self-Explaining Decision Architecture\n## Self-Explaining Decision Architecture\n## Preliminary SEDA Implementation\n## Conclusion\n# Applying Causal ML to Predictive Navigation","[{\"question\":\"What is the dissertation’s central contribution to radar processing?\",\"answer\":\"It proposes first-principles machine learning integrated with radar signal processing to improve detection, tracking, and navigation, grounded in a structured decision architecture and supporting estimation components.\"},{\"question\":\"What is the Self-Explaining Decision Architecture (SEDA) and what parts does it include?\",\"answer\":\"SEDA comprises a pipeline of subsystems for data encoding, feature extraction, sequence interpretation, decision generation, and explanation generation, enabling interpretable decisions in the radar context.\"},{\"question\":\"How does the work apply causal machine learning to navigation?\",\"answer\":\"It formulates predictive navigation using motion models, then employs causal ML to forecast behavior, validating designs through simulation-based testing and experimental evaluation.\"}]","First Principles Machine Learning in Radar - Augmenting Signal Processing Techniques with Machine Learning for Detection, Tracking, and Navigation - Dissertation | PDF",1785819332,872,{"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},"first-principles-machine-learning-in-radar-augmenting-signal-processing-techniques-with-machine-learning-for-detection-tracking-and-navigation-dissertation","",{"@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/first-principles-machine-learning-in-radar-augmenting-signal-processing-techniques-with-machine-learning-for-detection-tracking-and-navigation-dissertation/123936/",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 is the dissertation’s central contribution to radar processing?","Question",{"text":75,"@type":76},"It proposes first-principles machine learning integrated with radar signal processing to improve detection, tracking, and navigation, grounded in a structured decision architecture and supporting estimation components.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the Self-Explaining Decision Architecture (SEDA) and what parts does it include?",{"text":80,"@type":76},"SEDA comprises a pipeline of subsystems for data encoding, feature extraction, sequence interpretation, decision generation, and explanation generation, enabling interpretable decisions in the radar context.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the work apply causal machine learning to navigation?",{"text":84,"@type":76},"It formulates predictive navigation using motion models, then employs causal ML to forecast behavior, validating designs through simulation-based testing and experimental evaluation.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]