Tightly Coupled Navigation and Wind Estimation for Mini UAVs

Abstract

A novel nonlinear filter for the state-space model based on aided Inertial Navigation System, 1D Pitot static tube, angle-of-attack and angle-of-sideslip sensors is proposed. The solution, based on Multiplicative Kalman filtering, estimates in real-time the orientation, the velocity and the position of an Unmanned Aerial Vehicle along with sensor bias and 3D wind components. This paper describes and justifies the designed tightly coupled estimation scheme with both theoretical and experimental considerations. We validate then the whole approach for a mini UAV controlled through the well-known Paparazzi autopilot system which we equip with a set of low-cost sensors (accelerometers, gyros, GPS, magnetometer, barometer, 1D Pitot static tube and angular sensors), by successfully comparing the estimates obtained from real flight data with the 3D wind ground truth provided from a 60-m weather measurement tower.

Wind Measurement and Simulation Techniques in Multi-Rotor Small Unmanned Aerial Vehicles

“…A considerable amount of effort has been invested in studying wind and airspeed estimation in fixed-wing sUAVs [19], [32], [33], [36]- [38]. In general, these studies make use of measurements from flow sensors such as pitot tubes (discussed in Section II-A) and the (non-linear) dynamic model of a fixed-wing sUAV to perform estimation of wind speed and sUAV airspeed.…”

“…The wind observer combined the model of the fixed-wing aircraft with measurements from a GPS, IMU, and a pitot tube. In [38], a nonlinear state estimator was used, based on multiplicative Kalman filtering [41], to provide real-time estimates of orientation, velocity, and position of the flying vehicle along with 3D wind velocity components. In addition to the fixed-wing sensor suite, the method also used measurements from an angle-of-attack and angle-ofsideslip sensors attached to the aircraft.…”

Globally exponentially stable wind estimation for small fixed-wing UAVs using standard sensors

“…The filters that are tested with data from simulations and experiments are • EKF: A classic EKF implementation using the dynamic model (2) and the measurement model (1) • KF [4]: The KF estimator presented in [4] • 3SF2: The second stage estimator of section III.B • 3SF3: The third stage estimator of section III.C Figure 1 shows the inputs and outputs of the 3SF developed in this paper and of the EKF used as benchmark. The velocity over ground might acquired from GNSS velocity measurements or other sensors or state esitamators.…”

“…For smaller airplanes, however, these solutions are often too large, heavy, or expensive, and commonly, only measurements from a Pitot-static tube are available. [1,2] employ unscented Kalman filter (UKF) and extended Kalman filter (EKF), respectively, to estimate the air-and ground-relative velocities from global navigation satellite system (GNSS) and airspeed measurements in addition to two vanes measuring AOA and SSA. [3] uses a multi-hole Pitot-static tube for the same purpose.…”

Low-altitude Sensing of Urban Atmospheric Turbulence with UAV

“…to heights of about 500 m, is the development of methods for diagnostics of the turbulent atmosphere with UAVs. The results of diagnostics of the speed of air mass flows with UAV are reported in [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35]. In [36][37][38][39][40][41][42][43][44][45][46], the fundamental possibility of measuring the turbulence spectra with fixed-wing UAVs of various sizes and weights was shown.…”