Synthetic Aperture Radar (SAR) Meets Deep Learning

This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

RF MEMS technology for millimeter-wave radar sensors

The dissertation discusses RF MEMS technology for millimeter-wave radar sensors. RF MEMS, which stands for radio frequency micro-electromechanical system, and radar sensor fundamentals are briefly introduced. Of particular interest are: Firstly, a self-aligned fabrication process for capacitive fixed-fixed beam RF MEMS components is disclosed. It enables scaling of the critical dimensions and reduces the number of processing steps by 40% as compared with a conventional RF MEMS fabrication process. Scaling of the critical dimensions of RF MEMS components offers the potential of submicrosecond T/R switching times. RF MEMS varactors with beam lengths of 30 μm are demonstrated using the self-aligned fabrication process, and the performance of a 4 by 4 RF MEMS varactor bank is discussed as well. At 20 GHz, the measured capacitance values range between 180.5 fF and 199.2 fF. The measured capacitance ratio is 1.15, when a driving voltage of 35 V is applied, and the measured loaded Q factor ranges between 14.5 and 10.8. The measured cold-switched power handling is 200 mW. The simulated switching time is 354.6 ns. Secondly, an analog RF MEMS slotline TTD phase shifter is disclosed, for use in conjunction with ultra wideband (UWB) tapered slot antennas, such as the Vivaldi aerial and the double exponentially tapered slot antenna. It is designed for transistor to transistor logic (TTL) bias voltage levels and exhibits a measured phase shift of 28.2°/dB (7.8 ps/dB) and 59.2°/cm at 10 GHz, maintaining a 75 Ω; differential impedance match (S11dd ≤ -15.8 dB). The input third-order intercept point (IIP3) is 5 dBm at 10 GHz for a Δf of 50 kHz, measured in a 100 Ω differential transmission line system.Ph.D.Electrical EngineeringUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/61348/1/vcaeken.pd

Multichannel techniques for 3D ISAR

This thesis deals with the challenge of forming 3D target reconstruction by using spatial multi-channel ISAR configurations. The standard output of an ISAR imaging system is a 2D projection of the true three-dimensional target reflectivity onto an image plane. The orientation of the image plane cannot be predicted a priori as it strongly depends on the radar-target geometry and on the target motion, which is typically unknown. This leads to a difficult interpretation of the ISAR images. In this scenario, this thesis aim to give possible solutions to such problems by proposing three 3D processing based on interferometry, beamforming techniques and MIMO InISAR systems. The CLEAN method for scattering centres extraction is extended to multichannel ISAR systems and a multistatic 3D target reconstruction that is based on a incoherent technique is suggested

Multichannel techniques for 3D ISAR

This thesis deals with the challenge of forming 3D target reconstruction by using spatial multi-channel ISAR configurations. The standard output of an ISAR imaging system is a 2D projection of the true three-dimensional target reflectivity onto an image plane. The orientation of the image plane cannot be predicted a priori as it strongly depends on the radar-target geometry and on the target motion, which is typically unknown. This leads to a difficult interpretation of the ISAR images. In this scenario, this thesis aim to give possible solutions to such problems by proposing three 3D processing based on interferometry, beamforming techniques and MIMO InISAR systems. The CLEAN method for scattering centres extraction is extended to multichannel ISAR systems and a multistatic 3D target reconstruction that is based on a incoherent technique is suggested

多周波ステップレーダにおける目標検出のための信号処理に関する研究

電気通信大学202

Abstracts on Radio Direction Finding (1899 - 1995)

The files on this record represent the various databases that originally composed the CD-ROM issue of "Abstracts on Radio Direction Finding" database, which is now part of the Dudley Knox Library's Abstracts and Selected Full Text Documents on Radio Direction Finding (1899 - 1995) Collection. (See Calhoun record https://calhoun.nps.edu/handle/10945/57364 for further information on this collection and the bibliography). Due to issues of technological obsolescence preventing current and future audiences from accessing the bibliography, DKL exported and converted into the three files on this record the various databases contained in the CD-ROM. The contents of these files are: 1) RDFA_CompleteBibliography_xls.zip [RDFA_CompleteBibliography.xls: Metadata for the complete bibliography, in Excel 97-2003 Workbook format; RDFA_Glossary.xls: Glossary of terms, in Excel 97-2003 Workbookformat; RDFA_Biographies.xls: Biographies of leading figures, in Excel 97-2003 Workbook format]; 2) RDFA_CompleteBibliography_csv.zip [RDFA_CompleteBibliography.TXT: Metadata for the complete bibliography, in CSV format; RDFA_Glossary.TXT: Glossary of terms, in CSV format; RDFA_Biographies.TXT: Biographies of leading figures, in CSV format]; 3) RDFA_CompleteBibliography.pdf: A human readable display of the bibliographic data, as a means of double-checking any possible deviations due to conversion

Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences

The aim of the Special Issue “Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences” was to present a selection of innovative studies using hyperspectral imaging (HSI) in different thematic fields. This intention reflects the technical developments in the last three decades, which have brought the capacity of HSI to provide spectrally, spatially and temporally detailed data, favoured by e.g., hyperspectral snapshot technologies, miniaturized hyperspectral sensors and hyperspectral microscopy imaging. The present book comprises a suite of papers in various fields of environmental sciences—geology/mineral exploration, digital soil mapping, mapping and characterization of vegetation, and sensing of water bodies (including under-ice and underwater applications). In addition, there are two rather methodically/technically-oriented contributions dealing with the optimized processing of UAV data and on the design and test of a multi-channel optical receiver for ground-based applications. All in all, this compilation documents that HSI is a multi-faceted research topic and will remain so in the future