This paper describes the data fusion approach that is developed for navigation of autonomous unmanned aerial vehicles
(UAVs) for those applications where the Global Positioning System (GPS) signals are denied. Example scenarios
include navigation under interference and jamming and urban navigation missions. The system architecture is
biologically inspired and exploits measurements that are utilized by flying insects for self-localization purposes. The
data fusion algorithm implements the Kalman filter mechanization that fuses INS data (position velocity and attitude),
optical flow data from a monocular downward looking visual system (scaled body-frame vehicle velocity components),
and compass measurements (azimuth angle). Kalman filter measurement observables are formulated in a complimentary
form, i.e., as differences between optical flow/compass measurements and INS states that are projected into the
measurement domain. The filter estimates inertial error states and error in the flight height. We present the navigation
solution architecture and demonstrate its feasibility using simulations and actual data experiments. Also, we compare our
results to a data fusion algorithm that fuses airspeed and optical flow measurements.
SC894: Introduction to INS and INS-Based Integrated Navigation
Inertial navigation technology is currently being used in a variety of application areas that range from highly specialized military systems to mass-market consumer devices. This course provides attendees with a knowledge of inertial navigation principles, overviews integration of the inertial navigation system (INS) with other sensors, and gives various examples of practical INS applications. The course teaches basic INS computations, describes INS error behavior, classifies inertial sensors and discusses techniques for integrating the INS with other navigation technologies. Practical examples are given to illustrate the use of the INS in various application areas including a stand-alone airborne INS, GPS/INS integration, INS/Electro optical integration and INS/Lidar applications.