Inexpensive Microwave Radiometer-Based Passive Sensing System for Short-Term Forecasting of Satellite Link Quality

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: N/A
Agency Tracking Number: 36187
Amount: $99,837.00
Phase: Phase I
Program: SBIR
Awards Year: 1997
Solicitation Year: N/A
Solicitation Topic Code: N/A
Solicitation Number: N/A
Small Business Information
2898 30th Street, Boulder, CO, 80301
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Dr. Frederick Solheim
 (303) 449-9192
Business Contact
Phone: () -
Research Institution
It is known that atmospheric observations by microwave radiometers can predict and measure satellite link losses. These instruments, however, have historically been costly, bulky, and somewhat fragile, and therefore not desirable for monitoring satellite link conditions. Radiometries is proposing herein to modify an inexpensive monolithic microwave integrated circuit (MMIC) receiver currently manufactured for the telecomunications industry into a radiometer. Radiometries has investigated radiometer, microbarometer and surface temperature data from several field experiments, and finds that gradients in the water vapor field occur, and high frequency components appear on all of these data in advance of unstable weather, cloudiness, and rain events. These precursors are presumably due to increase in water vapor structure, and to propagation of acoustic and gravity waves from the impending weather. We believe that these signatures can be characterized with wavelet transforms, and therefore be utilized as predictors of buildup of weather. Radiometries Corporation proposes a novel method herein for forecasting satellite link losses. Conically scanned low angle data from a water vapor/cloud liquid water radiometer, surface wind and direction, surface temperature, satellite link signal to noise (Eb/No) information, and a sensitive barometer date will be processed utilizing neural network methods. We believe this will result in a robust predictive system.

* Information listed above is at the time of submission. *

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