Browsing by Subject "Synthetic aperture radar"

Now showing 1 - 6 of 6

Adaptive multiscale estimation for fusing image data
(2001) Slatton, Kenneth Clinton; Crawford, Melba M.; Evans, Brian L. (Brian Lawrence), 1965-
Differentiable rendering for synthetic aperture radar imagery
(2021-08-13) Wilmanski, Michael Charles; Tamir, Jon (Jonathan I.); Wang, Zhangyang
Deep Neural Networks (DNNs) have been at the forefront of modern advancements in computer vision. In recent years, there have been efforts to incorporate more domain knowledge into DNN training and architectures by inserting signal processing functions into optimization pipelines. This can lead to DNNs that are trained more robustly and with limited data, as well as the capability to solve challenging inverse problems. Consequently, there is increasing interest in differentiable rendering, which allows explicitly modeling geometric priors and constraints into an optimization pipeline. Existing efforts in differentiable rendering have focused on imagery from electro-optical sensors, particularly conventional RGB-imagery. In this report, I propose an approach for differentiable rendering of Synthetic Aperture Radar imagery and demonstrate proof-of-concept experiments for the proposed method on the inverse problem of 3D Object Reconstruction. I also discuss limitations of the approach and next steps for future work.
Model-based signal processing for radar imaging of targets with complex motions
(2002) Li, Junfei; Ling, Hao
Model-based signal processing for inverse synthetic aperture radar (ISAR) imaging of targets with complex motions is proposed in this dissertation. Target motion is the most important issue in radar imaging of an unknown target. Although widely recognized as a promising tool in target recognition, ISAR imaging is not yet fully operational in real-world data processing. This is mainly due to the fact that an unknown target, especially a non-cooperative target could have complex motions. First, the performance of existing motion compensation algorithms is evaluated. For this purpose, three sets of radar images of an aircraft, including blind motion compensated images, truth motion compensated images, and predicted images using electromagnetic-code simulation are generated. The limitations of existing radar imaging algorithms are identified after a comparison of the radar images. vii The remaining part of this research focuses on how to overcome these limitations. This is achieved by performing target feature extraction in the presence of complex motions, including three-dimensional (3D) motion, non-rigid body motion and high order motion. For a target with non-planar motion, an algorithm based on the phase analysis of multiple point scatterers is proposed to blindly detect the existence of 3D motion from radar data. An adaptive feature extraction technique is also applied for 3D ISAR image reconstruction from undersampled radar data when the target pose data is known. For a target with non-rigid body motions, adaptive chirplet signal representation is used to first separate signals from the main body and the rotating parts. Better extraction of target geometric features and micro-Doppler features are achieved after individual processing of the separated signal. For a target with high order motions, genetic algorithms are used to replace exhaustive search to reduce the computational time. Throughout the research, the use of physical models is emphasized for better understanding of the radar data. Model-based processing, including adaptive joint time-frequency techniques and genetic algorithms are applied in the information extraction process. Point scatterer simulations are extensively used to test the correctness and to demonstrate the concept of the proposed methods. Results from measurement data are included to demonstrate the effectiveness of the work on realworld problems.
Radar interferometry measurement of land subsidence in El Paso, Texas
(2004) Leuro, Erick; Wilson, Clark R.; Buckley, Sean M.
This work presents the application of radar interferometry to detect land subsidence associated with water pumping in El Paso, Texas and adjacent areas. Geological and hydrological information are compared with the radar information to validate the results. An error treatment of the measurements is performed using the singular value decomposition technique. Synthetic aperture radar interferometry is a proven remote sensing technique to detect ground deformation in a three-dimensional scale with millimetric precision. It has been applied successfully in earthquake monitoring, volcano deformation, glacier movement and aquifer compaction. El Paso and Ciudad Juarez are located over the Hueco Bolson aquifer, an unconsolidated alluvial aquifer that consists of gravel, sand, silt and clay. Because of increased water pumping since the early 20th century, the water table has changed and subsidence has occurred. Measurements of land subsidence are reported from the 1950s, 1970s, and 1980s. This work considers subsidence in the 1990s
Spacecraft terrain relative navigation with synthetic aperture radar
(2020-08-10) Pogorelsky, Bryan Samuel; Zanetti, Renato, 1978-; Chen, Jingyi "Ann"
Situations can arise when a satellite cannot rely on external signals for navigation and must use onboard instruments to determine its position, velocity, and attitude in orbit. A spacecraft terrain relative navigation system is presented relying on measurements obtained from a synthetic aperture radar that are fused with inertial measurement unit data in a multiplicative extended Kalman filter. The method of processing the SAR images to retrieve information for the navigation filter is shown, including autofocusing and image geolocation steps. Monte Carlo simulation results are presented in which actual filter performance is compared to predicted filter performance. Specifically, two test cases, with varied initial attitude uncertainty, demonstrate that the SAR based terrain relative navigation system produces consistent state estimates and successfully bounds, or in some instances, significantly reduces the navigation uncertainty of the spacecraft throughout its trajectory by up to 95%
Surface deformation mapping and automatic feature detection over the Permian Basin using InSAR
(2022-06-08) Staniewicz, Scott; Chen, Jingyi "Ann"; Bettadpur, Srinivas; Hennings, Peter; Humphreys, Todd; Olson, Jon
The Permian Basin has become the United States' largest producer of oil and gas over the past decade. During the same time, it has experienced a sharp rise in the number of induced earthquakes. In order to better understand the damage potential from induced earthquakes, new data and monitoring approaches are critically needed. Interferometric Synthetic Aperture Radar (InSAR) is a remote sensing technique that measures surface deformation over broad areas with 10s-100s meter spatial resolution and up to millimeter-to-centimeter accuracy. These measurements can be used to derive information about Earth’s subsurface and assess induced seismic risks. However, it is difficult to perform basin-scale surface deformation mapping and automatic feature detection using InSAR because the signal-to-noise ratio (SNR) of the deformation signals compared to tropospheric noise is extremely low. It is common to assume that the Permian Basin is rigid enough that the subtle deformation associated with oil and gas production and wastewater injection are not detectable by InSAR. In this dissertation, we develop methods for characterizing tropospheric noise and its power spectral density directly from InSAR observations. We show that the tropospheric noise distribution is non-Gaussian, and a small portion of SAR scenes are corrupted by up to ±15 cm noise outliers associated with storms and heat waves. This finding is significant because most of the InSAR time series solutions are optimal only when noise follows a Gaussian distribution. We design robust and scalable time series algorithms to reconstruct the temporal evolution of surface deformation in this challenging scenario, and we achieved basin-wide millimeter-level accuracy based on independent GPS validation. We observe numerous subsidence and uplift features near active production and disposal wells, as well as linear deformation patterns associated with fault activities near clusters of induced earthquakes. Furthermore, we designed a new computer vision algorithm for detecting the size and location of unknown deformation features in large volumes of InSAR data. We are able to determine whether a detected feature is associated with tropospheric artifacts or real deformation signals based on a realistic tropospheric noise model derived from InSAR data.