Multisensor fusion for autonomous UAV navigation based on the Unscented Kalman Filter with Sequential Measurement Updates

This paper describes a new filtering framework of multisensor fusion and its application to the low-cost strapdown inertial navigation system of an Unmanned Aerial Vehicle (UAV). The navigation system fuses various sources of sensor information from low-cost sensor suites such as an Inertial Measurement Unit (IMU), a Global Positioning System (GPS), and a three-axis magnetometer in the new framework of the Unscented Kalman Filter with Sequential Measurement Updates (SMU-UKF). In particular, sensor measurements can be easily fused together regardless of the number of sensors, sensor update rates, and sensor data dimensions. The performance and error analysis of the integrated navigation system based on this new multisensor fusion filter are assessed in a realistic simulation environment by comparing performance with that of an existing Extended Kalman Filter-based navigation system.