SSCI proposes to develop, implement and test a collision detection system for unmanned aerial vehicles (UAV), referred to as the Asynchronous Sensor fuSion for Improved Safety of air Traffic (ASSIST). The ASSIST system is robust to ADS-B spoofing, and will associate the EO/IR and airborne radar (ABR) tracks generated onboard the UAV with those generated by the ground-based radar (GBR) to minimize false tracks generated by EO/ABR due to clutter. Under this project, we plan to leverage our FORECAST technology (Fast On-line pREdiCtion of Aircraft State Trajectories) that fuses the ground-based radar information with airborne radar and transponder data to achieve accurate track generation and efficient prediction of potential NMACs in a high-density airspace. We will also leverage our SAFESEE (Sense and Avoid using Fusion and Expansion SEgmEntation) technology - a collision detection system that uses pixel-level fusion of EO/IR optical-flow features to achieve robust probability of detection and low FAR under realistic operating conditions. SAFESEE has been recently flight tested at the Air Force Bombing Range at Avon Park, FL. Under our FORECAST project we developed a capability of simulating communication delay between the ground station and the UAV. We plan to extend this capability and carry out a study of effects of the communication delay on the ASSIST system. Specific Phase I tasks include: (i) Acquire target tracks and FAR related to existing capabilities from NASA; (ii) Develop, implement and test the ASSIST system; and (iii) Carry out a trade study of the effect of the communication delay on the ASSIST system. In Phase II we plan to carry out extensive analysis and simulation testing of the ASSIST system, and arrive at a flight testing plan for the continuation of the work beyond Phase II.