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Measurements-Based Radar Signature Modeling: An Analysis Framework (MIT Lincoln Laboratory Series)-电子书百科大全

Measurements-Based Radar Signature Modeling: An Analysis Framework (MIT Lincoln Laboratory Series)

Measurements-Based Radar Signature Modeling: An Analysis Framework (MIT Lincoln Laboratory Series)
by: Joseph T. Mayhan (Author),John A. Tabaczynski(Author)
Publisher:The MIT Press
Publication Date: May 14, 2024
Language:English
Print Length:512 pages
ISBN-10:0262048116
ISBN-13:9780262048118
Book Description
A high-level text that synthesizes diverse research areas for characterizing objects (targets) from radar data and establishes a novel analysis framework for a class of signal processing techniques useful for high-resolution radar signature modeling.The only text to integrate a diverse body of work on characterizing objects (targets) from radar data into a common analysis framework, this book brings together the results of research papers and technical reports providing improved resolution and precision in radar target signature modeling and target motion solutions. It offers comprehensive coverage related to basic radar concepts, signal representation, and radar measurements; the development of advanced analysis tools essential for high-resolution signature modeling; the development of novel wideband and narrowband radar imaging techniques; the application of 2D spectral estimation theory to wideband signal processing; ultra-wideband scattering phenomenology and sparse-band sensor data fusion; and the integration of field measurements into the radar signature modeling process. The analysis techniques developed in the text provide the framework for a novel approach, called measurements-based modeling (MBM), to model target signatures by incorporating measurement data into the signature model of the target. Extensive examples throughout compare the performance of the new techniques with that of conventional analysis techniques.The first systematic, comprehensive synthesis of wide-ranging research areas for characterizing targets from radar data A deeply researched, lucid presentation enriched by extensive illustrations and examples An essential reference for experts in radar and signal processing, professional engineers in related fields, and graduate students
About the Author
A high-level text that synthesizes diverse research areas for characterizing objects (targets) from radar data and establishes a novel analysis framework for a class of signal processing techniques useful for high-resolution radar signature modeling.The only text to integrate a diverse body of work on characterizing objects (targets) from radar data into a common analysis framework, this book brings together the results of research papers and technical reports providing improved resolution and precision in radar target signature modeling and target motion solutions. It offers comprehensive coverage related to basic radar concepts, signal representation, and radar measurements; the development of advanced analysis tools essential for high-resolution signature modeling; the development of novel wideband and narrowband radar imaging techniques; the application of 2D spectral estimation theory to wideband signal processing; ultra-wideband scattering phenomenology and sparse-band sensor data fusion; and the integration of field measurements into the radar signature modeling process. The analysis techniques developed in the text provide the framework for a novel approach, called measurements-based modeling (MBM), to model target signatures by incorporating measurement data into the signature model of the target. Extensive examples throughout compare the performance of the new techniques with that of conventional analysis techniques.The first systematic, comprehensive synthesis of wide-ranging research areas for characterizing targets from radar data A deeply researched, lucid presentation enriched by extensive illustrations and examples An essential reference for experts in radar and signal processing, professional engineers in related fields, and graduate students Read more

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