Channel Model Tool Based on Bidirectional Analytic Ray Tracing and Radiative Transfer (CBAR)
Abstract
Test and evaluation (T&E) of airborne test articles is routinely carried out in Major Range and Test Facility Bases across United States. Critical test data is obtained through a telemetry link. The link quality, and thereby the ability to record test data at a ground station, strongly depends on the channel conditions. Typical aeronautical telemetry channels suffer from multipath fading, resulting in link dropouts or outage, and flight tests are very expensive. Therefore, a channel model tool that allows a user to understand the telemetry performance of a test article flying over a known geographical region is highly desirable. Existing channel model capabilities are sufficient for conducting flight tests over water or flat ground such as desert and dry lake bed. However, they cannot fulfill requirements of more sophisticated test scenarios involving low flying unmanned air vehicles and helicopters tested over water at high sea states, in hilly terrain, or even over urban environment. Some of these tests may be deliberately carried out under weather conditions such as rain. Therefore, it is necessary to have an improved radio frequency channel modeling tool that faithfully capture impact of atmospheric conditions, complex terrain reflection, and foliage scattering; and quickly generate performance evaluation results. In this project, IAI is developing a software tool called CBAR (Channel Model Tool Based on Bidirectional Analytic Ray Tracing and Radiative Transfer) that offers the benefit of high-fidelity channel characterization accounting for terrain and atmospheric effects, resulting in statistical and deterministic channel models that can be incorporated easily into a wireless network performance simulation tool.
Document Details
- Document Type
- Technical Report
- Publication Date
- May 16, 2012
- Accession Number
- ADA583482
Entities
People
- Chujen Lin
- David Mihalcik
- Feng Xu
- Kip Temple
- Mark Radke
- Peter Weed
- Satya Ponnaluri
- Tom Young
- Yik-kiong Hue