Multi-Sensor Data Integration for Autonomous Sense and Avoid

To operate Unmanned Aerial Systems (UAS) freely in the National Airspace System (NAS), an on-board sense-and-avoid (SAA) capability equivalent to or better than manned aircraft will be required. Under U.S. Air Force Research Laboratory (AFRL) sponsorship, Northrop Grumman Corporation (NGC) has been developing a scalable autonomous SAA system using a comprehensive sensor suite comprising Traffic Alert and Collision Avoidance System (TCAS) and Automatic Dependent Surveillance Broadcast (ADS-B) for detecting cooperative intruders as well as radar and electro-optical (EO) sensors for detecting noncooperative intruders. This paper focuses on the sensor data integration (SDI) portion of the autonomous SAA system in the areas of design objective, architectural and algorithmic approach, and flight test results.