[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122804-en":3,"doc-seo-122804-105":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},122804,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Machine Learning for Kalman Filter Tuning Prediction in GPS/INS Trajectory Estimation","Machine Learning for Kalman Filter Tuning Prediction in GPS/INS Trajectory Estimation develops a modular application concept that applies ML/AI to automatically tune Kalman-Filter parameters used in post-flight trajectory estimation. The work focuses on designing a plug-in style skeleton so multiple AI/ML modules can be created for tuning-switch prediction. The proposed framework includes training and real-time components, plus analyst/client integration to support operational workflow in post-flight settings.","California State University, San Bernardino  \nCSUSB ScholarWorks  \n\n| Electronic Theses, Projects, and Dissertations | Office of Graduate Studies |\n| --- | --- |\n| 12-2023\u003Cbr>Machine Learning for Kalman Filter Tuning Prediction in GPS/INS Trajectory Estimation\u003Cbr>Peter Wright\u003Cbr>Follow this and additional works at: [https://scholarworks.lib.csusb.edu/etd](https://scholarworks.lib.csusb.edu/etd)\u003Cbr> Part of the Computational Engineering Commons, and the Computer and Systems Architecture Commons |  |\n\nRecommended Citation  \nWright, Peter, \"Machine Learning for Kalman Filter Tuning Prediction in GPS/INS Trajectory Estimation\"(2023) . Electronic Theses, Projects, and Dissertations. 1830.  \n[https://scholarworks.lib.csusb.edu/etd/1830](https://scholarworks.lib.csusb.edu/etd/1830)  \nThis Project is brought to you for free and open access by the Office of Graduate Studies at CSUSB ScholarWorks. It has been accepted for inclusion in Electronic Theses, Projects, and Dissertations by an authorized administrator of CSUSB ScholarWorks. For more information, please contact [scholarworks@csusb.edu](scholarworks@csusb.edu).  \nMACHINE LEARNING FOR KALMAN FILTER TUNING  \nPREDICTION IN GPS/INS TRAJECTORY ESTIMATION  \nA Project Presented to the Faculty of  \nCalifornia State University, San Bernardino  \nIn Partial Fulfillment of the Requirements for the Degree Master of Science in  \nComputer Science  \nby Peter Wright  \nDecember 2023  \nMACHINE LEARNING FOR KALMAN FILTER TUNING  \nPREDICTION IN GPS/INS TRAJECTORY ESTIMATION  \nA Project Presented to the Faculty of  \nCalifornia State University, San Bernardino  \nby  \nPeter Wright December 2023 Approved by:  \nBilal Khan PhD, Committee Chair, Computer Science  \nFadi Muheidat PhD, Committee Member  \nJennifer Jin PhD, Committee Member  \n© 2023 Peter Wright  \nABSTRACT  \nThis project is an exploration and implementation of an application using Machine Learning (ML) and Artificial Intelligence (AI) techniques which would be capable of automatically tuning Kalman-Filter parameters used in post-flight trajectory estimation software at Edwards Air Force Base (EAFB), CA. The scope of the work in this paper is to design and develop a skeleton application with modular design, where various AI/ML modules could be developed to plug-in to the application for tuning-switch prediction.  \nTABLE OF CONTENTS  \nABSTRACT .......................................................................................................... iii  \nLIST OF FIGURES ............................................................................................... v  \nCHAPTER ONE: INTRODUCTION ...................................................................... 1  \nDesign Considerations ............................................................................... 1  \nCHAPTER TWO: BACKGROUND ....................................................................... 3  \nThe Problem .............................................................................................. 3  \nThe Desired Solution ................................................................................. 4  \nCHAPTER THREE: SCOPE................................................................................. 6  \nCHAPTER FOUR: RELATED WORK................................................................... 8  \nCHAPTER FIVE: PROPOSED FRAMEWORK..................................................... 9  \nMajor Considerations ............................................................................... 10  \nTraining Application ................................................................................. 12  \nReal-Time Application .............................................................................. 17  \nAnalyst/Client Integration ......................................................................... 20  \nCHAPTER SIX: PRELIMINARY RESULTS ........................................................ 21  \nPromising Results ...............................................","cbCaicDjRsVjSzVa","https://ap.wps.com/l/cbCaicDjRsVjSzVa","pdf",1561388,1,34,"English","en",105,"# Abstract\n# List of Figures\n# Chapter One: Introduction\n## Design Considerations\n# Chapter Two: Background\n## The Problem\n## The Desired Solution\n# Chapter Three: Scope\n# Chapter Four: Related Work\n# Chapter Five: Proposed Framework\n## Major Considerations\n## Training Application\n## Real-Time Application\n## Analyst/Client Integration\n# Chapter Six: Preliminary Results\n## Promising Results\n## Less than Promising Results\n# Chapter Seven: Conclusions and Future Work\n# References","[{\"question\":\"What problem does the project address?\",\"answer\":\"The project targets the need to tune Kalman-Filter parameters for post-flight trajectory estimation in GPS/INS systems.\"},{\"question\":\"What is the proposed solution approach?\",\"answer\":\"It designs and develops a modular skeleton application where ML/AI modules can plug in to perform tuning-switch prediction.\"},{\"question\":\"How is the system organized for use?\",\"answer\":\"The framework includes both training and real-time application components, along with analyst/client integration for workflow support.\"}]","Machine Learning for Kalman Filter Tuning Prediction in GPS/INS Trajectory Estimation | 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