Moon-Based Synthetic Aperture Radar

A Signal Processing Prospect
1. Auflage 2024
ISBN: 978-1-032-31168-5
Verlag: CRC Press

Buch, Englisch, 352 Seiten, Format (B × H): 183 mm x 260 mm, Gewicht: 1040 g

Reihe: SAR Remote Sensing

Lunar explorations have received increasing attention in recent years with tremendous application values, including using the Moon as a remote sensing platform for Earth observation. As an active sensor, the Synthetic Aperture Radar (SAR) can detect changes in the atmosphere, terrain, and ocean. Moon-based SAR, complementary to the spaceborne SAR systems, expands our capabilities of watching and understanding the Earth. This book explains the Moon-Earth observation geometry, generic parameters, image focusing, and outlook using the Moon-based SAR. Written as a SAR imaging of Earth on the lunar-based platform, it makes it an essential reference to those interested in planetary and Earth sciences.

FEATURES

- Uses the Moon as a remote sensing platform for Earth observation

- Explains how to obtain a high spatial resolution with a short revisit time using the Moon-based SAR

- Covers the observation geometry, range and signal models, two-dimensional signal spectrum, and focusing algorithms for the Moon-based SAR

- Presents a detailed analysis of sources of phase errors in the Moon-based SAR signal

- Includes global case studies and introduces conceptual ideas for further research

This book is intended for senior graduate students, professional researchers, and engineers studying and working in the fields of lunar exploration and remote sensing applications, especially when dealing with high-orbit SAR studies.

Zielgruppe

Postgraduate, Professional, and Undergraduate Advanced

Autoren/Hrsg.

Xu, Zhen

Chen, Kun-Shan

Fachgebiete

• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Signalverarbeitung
• Technische Wissenschaften Umwelttechnik | Umwelttechnologie Umwelttechnik
• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Signalverarbeitung, Bildverarbeitung, Scanning
• Geowissenschaften Geologie Geologie
• Geowissenschaften Umweltwissenschaften Umwelttechnik
• Geowissenschaften Geologie GIS, Geoinformatik

Inhaltsverzeichnis

1. Background. 2. Moon-Based Earth Observation Geometry. 3. Generic Parameters in the MBSAR System. 4. Spatiotemporal Coverage in the MBSAR System. 5. Signal Model of the MBSAR System. 6. Signal Processing and Image Focusing. 7. Orbital Perturbation Effects in the MBSAR. 8. Ionospheric Effects on the L-Band MBSAR Imaging.

Über Autor(innen)

Zhen Xu joined the Department of Electronic and Information Engineering at Shantou University in 2020. He earned a PhD in cartography and geographic information system at the Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, and the University of Chinese Academy of Sciences in 2020. His research interests include the system design and signal processing of Moon-based SAR. He was awarded the Outstanding Talent of Shantou University and the High-Level Talent of Shantou City. Dr. Xu also received Young Scientist Awards from the International Conference on Space, Aeronautical, and Navigational Electronics (ICSANE) in 2018 and the General Assembly and Scientific Symposium of the International Union of Radio Science (URSI GASS) in 2023.

Kun-Shan Chen is a nationally distinguished professor at Nanjing University in China. He earned a PhD in electrical engineering at the University of Texas at Arlington in 1990. His research interests include microwave remote sensing theory, modeling, systems, measurement, intelligent signal processing, and data analytics for radar. He has authored and co-authored over 500 refereed journals and conference papers. He is the author of several books published by CRC Press. His academic activities are numerous, spanning 30 years. Dr. Chen, an IEEE Fellow, received the 2021 IEEE GRSS Fawwaz Ulaby Distinguished Achievement Award for his contributions to microwave scattering and emission modeling of rough surface and radar image simulation and understanding.