[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120169-en":3,"doc-seo-120169-105":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},120169,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine learning-based design of Doppler tolerant radar","Machine learning theory is used to design a radar detector that remains robust to Doppler shifts. The radar system targets signal conditions that are difficult for conventional optimal detector design, including transmitted noise waveforms and one-bit quantization at the receiver. Results show that the one-bit receiver matches the performance of the derived square-law sign correlator detector. The learning-based detector further achieves Doppler tolerance by selecting training data representing expected Doppler shifts and interference, with optional improvements using prior estimates of Doppler and noise covariance.","New Jersey Institute of Technology  \nDigital Commons @ NJIT  \n\n| Dissertations | Electronic Theses and Dissertations |\n| --- | --- |\n| 5-31-2024\u003Cbr>Machine learning-based design of doppler tolerant radar\u003Cbr>Kyle Peter Wensell\u003Cbr>New Jersey Institute of Technology, [KyleWensell@outlook.com](KyleWensell@outlook.com)\u003Cbr>Follow this and additional works at: [https://digitalcommons.njit.edu/dissertations](https://digitalcommons.njit.edu/dissertations)\u003Cbr> Part of the Automotive Engineering Commons, Data Science Commons, Signal Processing Commons, and the Systems and Communications Commons |  |\n\nRecommended Citation  \nWensell, Kyle Peter, \"Machine learning-based design of doppler tolerant radar\" (2024) . Dissertations. 1761.  \n[https://digitalcommons.njit.edu/dissertations/1761](https://digitalcommons.njit.edu/dissertations/1761)  \nThis Dissertation is brought to you for free and open access by the Electronic Theses and Dissertations at Digital Commons @ NJIT. It has been accepted for inclusion in Dissertations by an authorized administrator of Digital Commons @ NJIT. For more information, please [contact digitalcommons@njit.edu](contact digitalcommons@njit.edu).  \nCopyright Warning & Restrictions  \nThe copyright law of the United States (Title 17, United States Code) governs the making of photocopies or other reproductions of copyrighted material.  \nUnder certain conditions specified in the law, libraries and archives are authorized to furnish a photocopy or other reproduction. One of these specified conditions is that the photocopy or reproduction is not to be “used for any purpose other than private study, scholarship, or research.”If a, user makes a request for, or later uses, a photocopy or reproduction for purposes in excess of “fair use” that user may be liable for copyright infringement,  \nThis institution reserves the right to refuse to accept a copying order if, in its judgment, fulfillment of the order would involve violation of copyright law.  \nPlease Note: The author retains the copyright while the New Jersey Institute of Technology reserves the right to distribute this thesis or dissertation  \nPrinting note: If you do not wish to print this page, then select“Pages from: first page \\# to: last page \\#” on the print dialog screen  \nThe Van Houten library has removed some of the personal information and all signatures from the approval page and biographical sketches of thesesand dissertations in order to protect the identity of NJIT graduates and faculty.  \nABSTRACT  \nMACHINE LEARNING-BASED DESIGN  \nOF DOPPLER TOLERANT RADAR  \nby  \nKyle Peter Wensell  \nIn this work, machine learning theory is applied to the design of a radar detector in order to train a machine learning-based detector that is robust against Doppler shifts. The radar system is designed to work with data that would be otherwise intractable to conventional optimal detector design, such as transmitted noise waveforms and the effects of one-bit quantization at the receiver. The detection performance of the one-bit receiver is shown to match the performance of the derived square-law sign correlator detector. The resulting learning-based detector also introduces Doppler tolerance to the system, which allows for the successful detection of a waveform that has been Doppler shifted due to sufficiently high target velocity. This is achieved by selecting the training data to best represent all expected Doppler shifts as well as interference effects. For further performance gains, the training data can be adjusted based on a priori estimates of the true Doppler and noise covariance values. Additionally, the advantages to using one-bit data are highlighted, which includes the reduction of computational power and memory requirements, and it is proven that this learning-based detector can be trained to detect waveforms even through the harsh non-linear effects of one-bit quantization.  \nMACHINE LEARNING-BASED DESIGN OF DOPPLER TOLERANT RADAR  \nby  \nKyle Peter Wensell  \nA","cbCaipYyIPdB0NJ7","https://ap.wps.com/l/cbCaipYyIPdB0NJ7","pdf",5765691,1,59,"English","en",105,"# Abstract\n## Doppler-tolerant radar detector design\n## One-bit receiver performance and correlator comparison\n## Training data selection and covariance-based refinements\n## Computational benefits of one-bit data","[{\"question\":\"How does the proposed approach address Doppler shifts in radar detection?\",\"answer\":\"A machine learning-based detector is trained so the system remains robust when waveforms experience Doppler shifts due to sufficiently high target velocity.\"},{\"question\":\"Why is one-bit quantization an important part of the design?\",\"answer\":\"The method is built to operate with one-bit receiver data, reducing computational power and memory requirements while still enabling effective detection.\"},{\"question\":\"How is detection performance validated for the one-bit receiver?\",\"answer\":\"The detection performance of the one-bit receiver is shown to match the performance of the derived square-law sign correlator detector.\"}]","Machine learning-based design of Doppler tolerant radar | 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does the proposed approach address Doppler shifts in radar detection?","Question",{"text":75,"@type":76},"A machine learning-based detector is trained so the system remains robust when waveforms experience Doppler shifts due to sufficiently high target velocity.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why is one-bit quantization an important part of the design?",{"text":80,"@type":76},"The method is built to operate with one-bit receiver data, reducing computational power and memory requirements while still enabling effective detection.",{"name":82,"@type":73,"acceptedAnswer":83},"How is detection performance validated for the one-bit receiver?",{"text":84,"@type":76},"The detection performance of the one-bit receiver is shown to match the performance of the derived square-law sign correlator 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