Passive Collision Detection for UAV Sense and Avoid Systems

Currently, Unmanned Aircraft Systems face limitations on their utilization in civil airspace because they do not have the capability to sense and avoid (SAA) other air traffic. Ability to detect collisions with low False Alarm and False Negative Rates are crucial for SAA. In Phase I, UtopiaCompression in collaboration with the University of California, Los Angeles successfully developed methods to optimally detect a collision in real-time using statistical decision theory and to solve the inverse problem of deriving the sensor specification for a given False Alarm Rate and False Negative Rate. In Phase II, this technology will be enhanced by extending the current collision detection method to the maneuvering targets case in real-time. The solution to the inverse problem will be improved by conducting field experiments and gathering data from real flight tests. The computational complexities and performance limitations will be better understood by conducting small scale flight tests in collaboration with Brigham Young University. This data will also be used to develop advanced noise models which will help in improving the System Operational Characteristic (SOC) curves. Monte-Carlo simulations will be performed to gather the relevant statistical information and study the effects of various systems parameters on the SOC curves