A Square-Root Unscented Kalman Filter for attitude and relative position estimation of a tethered unmanned helicopter

The use of tethered Unmanned Aircraft Systems (UAS) in aerial robotic applications is a relatively unexplored research field. In this work a numerically efficient implementation of a sigma-point Kalman filter is applied to the attitude and relative position estimation of a small-size tethered unmanned helicopter. For that purpose, the state prediction is performed using a kinematic process model driven by measurements of the inertial sensors (accelerometer and gyroscope) onboard the helicopter and the subsequent correction is done using information from additional sensors like magnetometer, radar altimeter and magnetic encoders measuring the tether orientation relative to the helicopter. Assuming the tether is kept taut by an actuated device on ground during the system operation, this approach avoids the need of a global positioning system (GPS) as the position is estimated relative to the anchor point. The filter performance is evaluated in simulations.