Model city for unmanned aerial systems and wireless research
Abstract
Our team from Virginia Tech (VT), including Wireless@VT and the Mid Atlantic Aviation Partnership (MAAP), propose a UAS Model City for RF Experimentation. The proposed instrumentation suite will enable both an indoor and outdoor testbed for RF experimentation with Unmanned Aircraft Systems (UAS). The testbed will enable not only UAS specific RF experimentation, but will leverage the UAS as reconfigurable nodes for RF experimentation relevant to a broad range of DoD applications. The UAS model city will enable a broad range of RF experiments in complex indoor and outdoor urban environments. The top two challenges to safe integration of UAS in the NAS or DoD battlespace include 1) development of Òsense and avoidÓ technologies, and 2) integrity of command and control links. The 2nd challenge becomes increasingly complex in urban areas where the Radio Frequency (RF) environment is saturated, complex, and difficult to model computationally. These urban areas however, represent a large portion of the UAS use cases for both civilian applications and the emerging DoD battlespace. Urban RF experimentation and characterization is currently limited by the lack of instrumentation at the test range and the challenges associated with conducting urban UAS flight operations. MAAP however, as one of the 6 UAS Test Sites, holds over 25 Certificates of Authorization (COAs) with the FAA to enable next generation UAS research, including a COA to operate UAS on campus at VT. Additionally, the Wireless@VT research group has a long history of operating a terrestrial cognitive radio testbed on the VT campus. The requested instrumentation consisting of Software Defined Radios (SDR), small UAS platforms, and supporting infrastructure, will provide the tools necessary for development and characterization of next generation air-to-ground and air-to-air RF architectures compatible with dense urban environments. This initiative will create a unique, modular, and flexible capability unique in academia. This abstract may be released to the public.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Sep 11, 2018
- Source ID
- W911NF1710246
Entities
People
- Carl Dietrich
Organizations
- Army Contracting Command
- United States Army
- Virginia Tech