Han Pan

This book explores the transformative methods that have revolutionized underwater robotics in recent decades. Focusing on practical algorithms and real-world applications, this book fulfills the pressing need for a comprehensive resource on underwater acoustic-optical perception. Highlighting advanced techniques such as Transformer models for sonar object tracking and detection, the ℓ1-total variation model, and acoustic-optical fusion, this book provides an in-depth exploration of sonar image processing, object detection, and tracking while addressing emerging challenges and future research directions. Rich with illustrative examples and computational case studies, this book serves as an essential reference for diverse audiences. University students pursuing underwater information processing and engineers specializing in deep-sea exploration, subsea operations, and sonar data analysis find it particularly invaluable. By bridging foundational concepts with cutting-edge innovations, this book offers the tools and insights necessary to advance understanding and implementation in underwater robotics and perception systems.

This book gives a concise and comprehensive overview of non-cooperative target tracking, fusion and control. Focusing on algorithms rather than theories for non-cooperative targets including air and space-borne targets, this work explores a number of advanced techniques, including Gaussian mixture cardinalized probability hypothesis density (CPHD) filter, optimization on manifold, construction of filter banks and tight frames, structured sparse representation, and others. Containing a variety of illustrative and computational examples, Non-cooperative Target Tracking, Fusion and Control will be useful for students as well as engineers with an interest in information fusion, aerospace applications, radar data processing and remote sensing.

This book gives a concise and comprehensive overview of non-cooperative target tracking, fusion and control. Focusing on algorithms rather than theories for non-cooperative targets including air and space-borne targets, this work explores a number of advanced techniques, including Gaussian mixture cardinalized probability hypothesis density (CPHD) filter, optimization on manifold, construction of filter banks and tight frames, structured sparse representation, and others. Containing a variety of illustrative and computational examples, Non-cooperative Target Tracking, Fusion and Control will be useful for students as well as engineers with an interest in information fusion, aerospace applications, radar data processing and remote sensing.