Sensor Fusion Algorithm for Airborne Autonomous Vehicle Collision Avoidance Applications

• Main Author: Doe, Julien Albert
• Format: Others
• Published: DigitalCommons@CalPoly 2018
• Subjects: radar; sensor fusion; clustering; Kalman filter
• Online Access: https://digitalcommons.calpoly.edu/theses/2004; https://digitalcommons.calpoly.edu/cgi/viewcontent.cgi?article=3467&context=theses

A critical ability of any aircraft is to be able to detect potential collisions with other airborne objects, and maneuver to avoid these collisions. This can be done by utilizing sensors on the aircraft to monitor the sky for collision threats. However, several problems face a system which aims to use multiple sensors for target tracking. The data collected from sensors needs to be clustered, fused, and otherwise processed such that the flight control system can make accurate decisions based on it. Raw sensor data, while filled with useful information, is tainted with inaccuracies due to limitations and imperfections of the sensor. Combined use of different sensors presents further issues in how to handle disagreements between sensor data. This thesis project tackles the problem of processing data from multiple sensors (in this application, a radar and an infrared sensor) on an airborne platform in order to allow the aircraft to make flight corrections to avoid collisions.