|6B.1 Advanced geostationary radar for hurricane monitoring and studies
By: Eastwood Im
Jet Propulsion Laboratory
The current Geostationary Operational Environ-mental Satellites (GOES) are equipped to make cloud top measurements only. In contrast, a millimeter-wave radar allows 3-D measurements of precipitation associated with hurricanes and other convective systems. It also provide important inputs for numerical weather prediction models for improving the accuracy of weather nowcasting and forecasting. Recently, a novel instrument concept and the associated critical technologies are being developed for a 35-GHz Doppler radar for detailed monitoring of hurricanes and severe storms from a geostationary orbit. This instrument is designed to be capable of producing rainfall rate at 13-km horizontal resolution and 300-m vertical resolution, and the line-of-sight Doppler velocity at 0.3 m/s precision, of the 3-D hurricane structure once per hour throughout its life cycle.
|6B.2 Crossbeam wind measurements with a phased array Doppler weather radar: theory
By: Richard J. Doviak
National Severe Storms Laboratory
and: Guifu Zhang
and: Tian-You Yu
University of Oklahoma
Doppler weather radars measure only the radial wind component of wind, and thus are limited in providing accurate information of damaging wind potential. The use of a phased array antenna opens the possibility that crossbeam winds can also be measured. This paper examines and compares two alternatives whereby a phased array weather radar can measure crossbeam winds. The theoretical accuracy of the quasi-horizontal component of the crossbeam wind for each of these alternatives is shown to be strongly dependent on turbulence intensity. Crossbeam winds can be measured with accuracies on the order of 2 m s-1 in less than 10 s if turbulence intensity is less than 1 m s-1.
|6B.3 An L-band SAR for Repeat Pass Deformation Measurements on a UAV Platform
By: Kevin Wheeler
We are proposing to develop a miniaturized polarimetric L-band synthetic aperture radar (SAR) for repeat-pass differential interferometric measurements of deformation for rapidly deforming surfaces of geophysical interest such as volcanoes or earthquakes that is to be flown on a unmanned aerial vehicle (UAV) or minimally piloted vehicle (MPV). With our proposed mechanical design approach for the radar electronics, the instrument can potentially be accommodated on a number of different applicable platforms. Upon surveying the capabilities and availabilities of UAVs and MPVs, the ALTAIR UAV and the Proteus aircraft appear to meet our criteria in terms of payload capabilities, flying altitude, and endurance. To support the repeat pass deformation capability it is necessary to control flight track capability of the aircraft to be within a specified 10 m tube with a goal of 1 m. This requires real-time GPS control of the autopilot to achieve these objectives that has not been demonstrated on these aircraft. Based on the Proteus and ALTAIR?s altitude of 13.7 km (45,000 ft), we are designing a fully polarimetric L-band radar with 80 MHz bandwidth and a 16 km range swath. The radar will have an active electronic beam steering antenna to achieve a Doppler centroid stability that is necessary for repeat-pass interferometry. This paper presents the radar configuration along with some of the trade studies for the platform and instrument.
|6B.4 SAR image formation algorithm with multipath reflectivity estimation
By: David A. Garren
and: J. Scott Goldstein
and: Danielle R. Obuchon
and: Robert R. Greene
and: Jan A. North
Lt. Col., USAF; SAF/ST
Recent analysis has resulted in an innovative technique for forming synthetic aperture radar (SAR) images without the multipath ghost artifacts that arise in traditional methods. This technique separates direct-scatter echoes in an image from echoes that are the result of multipath, and then maps each set of reflections to a metrically correct image space. Current processing schemes place the multipath echoes at incorrect (i.e., ghost) locations due to fundamental assumptions implicit in conventional array processing. Two desired results are achieved by use of this new Image Reconstruction Algorithm for Multipath Scattering (IRAMS). First, the intensities of the ghost returns are reduced in the primary image space, thereby improving the relationship between the image pattern and the physical distribution of the scatterers. Second, a higher dimensional image space that enhances the intensities of the multipath echoes is created which possesses characteristic information about the scene being imaged. These auxiliary "delay" image planes offer the potential of dramatically improving target detection and identification capabilities. This paper develops a robust IRAMS implementation that is based upon the cross-range drift in conventional SAR imagery of the multipath scattering events with respect to changes in the relative aspect angle. The resulting analysis is validated via simulated frequency response data that includes the effects of multipath scattering.
|6B.5 Belief Fusion, Pignistic Probabilities, and Information Content in Fusing Tracking Attributes
By: John J. Sudano
Abstract- In the design of information fusion systems, the reduction of computational complexity is a key design parameter for real-time implementations. One way to simplify the computations is to decompose the system into subsystems of non-correlated informational components, such as a qualitative informational component, a quantitative informational component, and a complement informational component.
A probability information content (PIC) variable  assigns an information content value to any set of system or sub-system probability distributions. The PIC variable is the normalized entropy computed from the probability distribution. This article derives a PIC variable for a subsystem represented by the complement probabilities. This article also derives a relationship between the PIC variable of sub-system components and the system informational PIC variable.
A series of pignistic probability transforms are presented that estimate the probability for any belief data set.
The Generalized Belief Fusion method of combining independent multi-source beliefs is presented.