Abstract
Navigation employing low cost MicroElectroMechanical Systems (MEMS) sensors in Unmanned Aerial Vehicles (UAVs) is an uprising challenge. One important part of this navigation is the right estimation of the attitude angles. Most of the existent algorithms handle the sensor readings in a fixed way, leading to large errors in different mission stages like take-off aerobatic maneuvers. This paper presents an adaptive method to estimate these angles using off-the-shelf components. This paper introduces an Attitude Heading Reference System (AHRS) based on the Unscented Kalman Filter (UKF) using the Fast Optimal Attitude Matrix (FOAM) algorithm as the observation model. The performance of the method is assessed through simulations. Moreover, field experiments are presented using a real fixed-wing UAV. The proposed low cost solution, implemented in a microcontroller, shows a satisfactory real time performance.
Highlights
Nowadays the industry is employing Unmanned Aerial Vehicles (UAVs) for mobile missions, specially in vigilance, monitoring and inspection scenarios [1,2]
Inertial Navigation System (INS), modern MEMS technologies are offering light and low cost solutions, which are more appropriate for the reduced space available for embedded systems in lightweight UAVs
This paper introduces Fast Optimal Attitude Matrix (FOAM) as observation model, which allows to set weights explicitly to the sensor readings, presenting clear advantages in the attitude estimation comparing with previous works that employ Three Axis Attitude Determination (TRIAD) and other sensor fixed-trust observations models
Summary
Nowadays the industry is employing UAVs for mobile missions, specially in vigilance, monitoring and inspection scenarios [1,2]. Space application works based on UKF are showing more robustness and accuracy than the EKF [5] Their computational cost is still higher than EKF, there are new sigma-points algorithms aiming to reduce this cost, making it comparable with EKF in attitude estimation [6]. This paper introduces FOAM as observation model, which allows to set weights explicitly to the sensor readings, presenting clear advantages in the attitude estimation comparing with previous works that employ TRIAD and other sensor fixed-trust observations models. For realism [12], perturbations in the form of high-frequency noise, sensor latencies, biases, scale factors and misalignment are considered in the simulator data During these simulations a maximum error of 1.0◦ is imposed on the pitch and roll attitude angles, and a maximum error of 4.0◦ on the yaw attitude angle. In the final section of the paper, some conclusions are drawn
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