A subsurface-imagingsynthetic-apertureradar(SISAR)has potential for application in areas as diverse as non-proliferation programs for nuclear weapons to environmentalmonitoring. However, subsurfaceimaging is complicated by propagation loss in the soil and surface-clutterresponse. Both the loss and surface-clutterresponsedepend on the operating frequency. This paperexamines several factorswhich providea basis for determiningoptimumfrequenciesand frequencyranges which will allow synthetic-apertureimaging of buried targets. No distinction can be made between objects at different heights when viewed with a conventional imaging radar (which uses a one-dimensional synthetic aperture), and the return from a buried object must compete with the return from the surface clutter. Thus, the signal-to-clutter ratio is an appropriate measure of performance for a SISA1L A parameter-based modeling approach is used to model the complex dielectric constant of the soil from measured data obtained from the literature. Theoretical random-surface scattering models, based on statistical solutions to Maxwell's equations, are used to model the clutter. These models are combined to estimate the signal-to-clutter ratio for canonical targets buried in several soil configurations. Results indicate that the HF spectrum (3-30 MHz), although it could be used to detect certain targets under some conditions, has limited practical value for use with SISAR, while the upper VHF through UHF spectrum (~100 MHz -1 GHz) shows the most promise for a general purpose SISAR system. Recommendations are included for additional research.
In recent years, increasing deployment of large wind-turbine farms has become an issue of growing concern for the radar community. The large radar cross section (RCS) presented by wind turbines interferes with radar operation, and the Doppler shift caused by blade rotation causes problems identifying and tracking moving targets. Each new wind-turbine farm installation must be carefully evaluated for potential disruption of radar operation for air defense, air traffic control, weather sensing, and other applications. Several approaches currently exist to minimize conflict between wind-turbine farms and radar installations, including procedural adjustroents, radar upgrades, and proper choice of low-impact wind-farm sites, but each has problems with limited effectiveness or prohibitive cost. An alternative approach, heretofore not techoically feasible, is to reduce the RCS of wind turbines to the extent that they can be installed near existing radar installations. This report summarizes efforts to reduce wind-turbine RCS, with a particular emphasis on the blades. The report begins with a survey of the wind-turbine RCS-reduction literature to establish a baseline for comparison. The following topics are then addressed: electromagnetic model development and validation, novel material development, integration into wind-turbine fabrication processes, integrated-absorber design, and wind-turbine RCS modeling. Related topics of interest, including alternative mitigation techoiques (procedural, at-the-radar, etc.), an introduction to RCS and electromagnetic scattering, and RCS-reduction modeling techniques, can be found in a previous report.
Recent data collections with the Sandia VHF-UHF synthetic-aperture radar have yielded surprising results; trees appear brighter in the images than expected! In an effort to understand this phenomenon, various small trees have been measured on the Sandia folded compact range with the inverse-synthetic-aperture imaging system. A compilation of these measurements is contained in this report.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.