Operator's and organizational maintenance manual : simulator, radar signal SM-674/UPM (NSN 6940-01-031-5887), and test adapter, radar signal MX-9848/APR-39(V) (NSN 5841-01-025-0379).

"16 June 1977."

Organizational, direct support, and general support maintenance repair parts and special tools lists for simulator, radar signal SM-674/UPM (NSN 6940-01-031-5887) and test adapter, radar set MX-9848/APR-39(V) 1 (NSN 5841-01-025-0379).

"May 1977."

Operator, organizational, DS and GS maintenance manual : destruction of equipment to prevent enemy use : Forward Area Alerting Radar (FAAR) system, AN/MPQ-49 NSN 1430-00-179-4199 (Radar Set), AN/GSQ-137 NSN 1430-00-179-5321 (TADDS), AN/MPM-57 NSN 4935-00-178-0833 (SMTS), AN/MPM-59 NSN 4935-00-178-0834 (OMTS).

"6 August 1975."

Transportability guidance : Foward Area Alerting Radar system (FAAR) : radar set, AN/MPQ 49, (NSN 1430-00-179-4199), truck, cargo, 1 1/4-ton, M561, (NSN 2320-00-873-5407), trailer, cargo, 3/4-ton, M101A1, (NSN 2330-00-898-6779), test set, radar, AN/MPM 57, (NSN 4935-00-178-0833).

"January 1976."

Operator's and organizational maintenance manual : radar set AN/TPN-18.

"November 1970."

Operator's and organizational maintenance manual including repair parts list : recorder-processor-viewers, radar mapping, RO-166/UP, RO-166B/UP, RO-166C/UP, RO-166D/UP, and RO-166E/UP.

"October 1966."

Locating a strand of the Seattle Fault Zone in southeast Seattle, Washington through topographic analysis, borehole analysis and ground penetrating radar

Contributing to the evaluation of seismic hazards, a previously unmapped strand of the Seattle Fault Zone (SFZ), cutting across the southwest side of Lake Washington and southeast Seattle, is located and characterized on the basis of bathymetry, borehole logs, and ground penetrating radar (GPR). Previous geologic mapping and geophysical analysis of the Seattle area have generally mapped the locations of some strands of the SFZ, though a complete and accurate understanding of locations of all individual strands of the fault system is still incomplete. A bathymetric scarp-like feature and co-linear aeromagnetic anomaly lineament defined the extent of the study area. A 2-dimensional lithology cross-section was constructed using six boreholes, chosen from suitable boreholes in the study area. In addition, two GPR transects, oblique to the proposed fault trend, served to identify physical differences in subsurface materials. The proposed fault trace follows the previously mapped contact between the Oligocene Blakeley Formation and Quaternary deposits, and topographic changes in slope. GPR profiles in Seward Park and across the proposed fault location show the contact between the Blakeley Formation and unconsolidated glacial deposits, but it does not constrain an offset. However, north-dipping beds in the Blakely Formation are consistent with previous interpretations of P-wave seismic profiles on Mercer Island and Bellevue, Washington. The profiles show the mapped location of the aeromagnetic lineament in Lake Washington and the inferred location of the steeply-dipping, high-amplitude bedrock reflector, representing a fault strand. This north-dipping reflector is likely the same feature identified in my analysis. I characterize the strand as a splay fault, antithetic to the frontal fault of the SFZ. This new fault may pose a geologic hazard to the region.

Consequences of incorrect sampling procedures in resonance-based radar target identification

Radar target identification based on complex natural resonances is sometimes achieved by convolving a linear time-domain filter with a received target signature. The filter is constructed from measured or pre-calculated target resonances. The performance of the target identification procedure is degraded if the difference between the sampling rates of the target signature and the filter is ignored. The problem is investigated for the natural extinction pulse technique (E-pulse) for the case of identifying stick models of aircraft.

Super-fast scanning technique for phased array weather radar aplications

The authors present a super-fast scanning (SFS) technique for phased array weather radar applications. The fast scanning feature of the SFS technique is described and its drawbacks identified. Techniques which combat these drawbacks are also presented. A concept design phased array radar system (CDPAR) is used as a benchmark to compare the performance of a conventional scanning phased array radar system with the SFS technique. It is shown that the SFS technique, in association with suitable waveform processing, can realise four times the scanning speed and achieve similar accuracy compared to the conventional phased array benchmark.

Integrating JERS-1 imaging radar and elevation models for mapping tropical rainforest communities in far North Queensland, Australia

The Wet Tropics World Heritage Area in Far North Queens- land, Australia consists predominantly of tropical rainforest and wet sclerophyll forest in areas of variable relief. Previous maps of vegetation communities in the area were produced by a labor-intensive combination of field survey and air-photo interpretation. Thus,. the aim of this work was to develop a new vegetation mapping method based on imaging radar that incorporates topographical corrections, which could be repeated frequently, and which would reduce the need for detailed field assessments and associated costs. The method employed G topographic correction and mapping procedure that was developed to enable vegetation structural classes to be mapped from satellite imaging radar. Eight JERS-1 scenes covering the Wet Tropics area for 1996 were acquired from NASDA under the auspices of the Global Rainforest Mapping Project. JERS scenes were geometrically corrected for topographic distortion using an 80 m DEM and a combination of polynomial warping and radar viewing geometry modeling. An image mosaic was created to cover the Wet Tropics region, and a new technique for image smoothing was applied to the JERS texture bonds and DEM before a Maximum Likelihood classification was applied to identify major land-cover and vegetation communities. Despite these efforts, dominant vegetation community classes could only be classified to low levels of accuracy (57.5 percent) which were partly explained by the significantly larger pixel size of the DEM in comparison to the JERS image (12.5 m). In addition, the spatial and floristic detail contained in the classes of the original validation maps were much finer than the JERS classification product was able to distinguish. In comparison to field and aerial photo-based approaches for mapping the vegetation of the Wet Tropics, appropriately corrected SAR data provides a more regional scale, all-weather mapping technique for broader vegetation classes. Further work is required to establish an appropriate combination of imaging radar with elevation data and other environmental surrogates to accurately map vegetation communities across the entire Wet Tropics.