Sparse Arrays for Radar, Sonar, and Communications
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          分類:英文書>自然科普>應用科學>電訊追蹤? 追蹤分類後,您會在第一時間收到分類新品通知。
 - 作者: Moeness G,Amin 追蹤 ? 追蹤作者後,您會在第一時間收到作者新書通知。
 - 出版社: Wiley-IEEE Press 追蹤 ? 追蹤出版社後,您會在第一時間收到出版社新書通知。
 - 出版日:2023/12/15
 
內容簡介
Specialized resource providing detailed coverage of recent advances in theory and applications of sparse arrays
Sparse Arrays for Radar, Sonar, and Communications discusses various design approaches of sparse arrays, including those seeking to increase the corresponding one-dimensional and two-dimensional virtual array apertures, as well as others that configure the arrays based on solutions of constrained minimization problems. The latter includes statistical bounds and signal-to-interference and noise ratio; in this respect, the book utilizes the recent strides made in convex optimizations and machine learning for sparse array configurability in both fixed and dynamic environments. Similar ideas are presented for sparse array-waveform design.
The book also discusses the role of sparse arrays in improving target detection and resolution in radar, improving channel capacity in massive MIMO, and improving underwater target localization in sonar. It covers different sparse array topologies, and provides various approaches that deliver the optimum and semi-optimum sparse array transceivers. .
Edited by a world-leading expert in Radar and Signal Processing and contributed to by world-class researchers in their respective fields, Sparse Arrays for Radar, Sonar, and Communications covers topics including: Utilizing sparse arrays in emerging technologies and showing their offerings in various sensing and communications applications Applying sparse arrays to different environments and obtain superior performances over conventional uniform arrays Solving the localization, beamforming, and direction-finding problems using non-uniform array structures for narrowband and wideband signals Designing sparse array structures for both stationary and moving platforms that produce physical and synthesized array apertures. Using deep neural networks that learn the underlying complex nonlinear model and output the sparse array configuration using representations of the input data spatio-temporal observations Solving for optimum sparse array configurations and beamforming coefficients in sensing using iterative convex optimization methods
Providing complete coverage of the recent considerable progress in sparse array design and configurations, Sparse Arrays for Radar, Sonar, and Communications is an essential resource on the subject for graduate students and engineers pursuing research and applications in the broad areas of active/passive sensing and communications.
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