Position and Velocity Optimal Sensor-based Navigation Filters for UAVs

The paper presents the design and performance evaluation of a novel navigation solution that merges low-rate delayed GPS measurements with high-rate linear acceleration, attitude, and angular velocity measurements to estimate, in three dimensions, linear motion quantities (position, linear velocity, an acceleration of gravity) of unmanned aerial vehicles (UAVs). The design is based on the continuous-discrete Kalman filter solution for an equivalent LTI realization and allows for the natural use of frequency weights to explicitly achieve adequate disturbance rejection and measurement noise attenuation on the state estimates. The proposed solution is optimal with respect to all quantities assuming exact angular measurements and, in the presence of noisy angular quantities, it outperforms classic solutions developed in inertial coordinates. Simulation results illustrate the achievable performance in the presence of realistic measurements, including noise and delays.