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Radio Propagation Prediction Software for Complex Mixed Path Physical Channels

Award Information

Department of Defense
Defense Advanced Research Projects Agency
Award ID:
Program Year/Program:
2004 / SBIR
Agency Tracking Number:
Solicitation Year:
Solicitation Topic Code:
Solicitation Number:
Small Business Information
Remcom Inc.
315 S. Allen St. Suite 416 State College, PA 16801-
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Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No
Phase 2
Fiscal Year: 2004
Title: Radio Propagation Prediction Software for Complex Mixed Path Physical Channels
Agency / Branch: DOD / DARPA
Contract: W31P4Q-04-C-R245
Award Amount: $746,560.00


Predicting the radio signal levels and coverage areas for Information Warfare System communication channels between base stations, vehicles and warfighters is a challenging problem. The channel may involve radio frequency interactions with hills, foliage, and buildings. The paths may involve long distances over hilly terrain or shorter distances involving interaction with urban building features. Many propagation paths may simultaneously involve hills, foliage, and urban areas. These mixed path links involving both urban (including indoor) features and rural (including foliage) terrain are beyond the capability of any existing physics-based propagation model. Development of fast and reliable mixed-path models valid over a wide range of communication frequencies will be the focus of the proposed effort. This development will by necessity bring together different types of models. Full wave models may be applied to predict the antenna pattern from an antenna on a vehicle, a warfighter crouched below a wall, or even for propagation paths involving terrain or atmospheric effects. Ray-based models are needed for complex interactions with urban features and for indoor situations. All of these need to be combined in new ways in order to obtain accurate mixed-path radio propagation predictions.

Principal Investigator:

Joseph Schuster
Electromagnetic Analyst

Business Contact:

Raymond Luebbers
Small Business Information at Submission:

315 S. Allen St. State College, PA 16801

EIN/Tax ID: 251728847
Number of Employees:
Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No