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SBIR/STTR

Hiawatha Aircraft Anti-Collision System, Phase I

Completed Technology Project

Project Introduction

For SUAVs, the FAA mandate to equip all aircraft with ADS-B Out transmitters by 1 January 2020 to support NextGen goals presents both logistical and mission security issues. Aircraft without ADS-B Out capabilities, ranging from commercial or general aviation aircraft with failed transponders to adversarial aircraft deliberately operating without required transponder equipment, will continue to exist within the general airspace and pose navigational hazards and tactical threats to SUAVs. Nokomis proposes to adapt its ultra-sensitive RF sensor system, called Hiawatha, to provide an unsurpassed trajectory management and anti-collision avoidance capability. The Hiawatha system provides flight-tested state-of-the-art ultra-sensitive RF detection, identification and geo-location performance. Nokomis will develop a system-level design of an anti-collision system to aid in trajectory management and safe traffic flow of autonomous UAV operations capable of meeting the SWaP requirements for incorporation into a representative SUAV payload platform. The RF-based traffic management and anti-collision avoidance system will be capable of monitoring the entire spectral range from 30 MHz to 3 GHz, while providing the necessary detection, identification, and locating abilities from all angles while operating in a non-interfering manner with other potential payloads. Specifically, Nokomis will demonstrate system sensitivity including long range detection and identification of representative UAV emissions, system geo-location and contact bearing capabilities, Doppler-based bearing and range to aircraft, and design Trajectory Prediction and Avoidance System and efficient Traffic Flow System for maintaining aircraft spacing. The Phase I effort proof of concept demonstrations will focus on a demonstration of the Hiawatha airborne system, detection, identification, and location of relevant targets using existing AoA algorithms, and existing source classification algorithms. More »

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