A Gaussian Process Regression-Based Multitarget Orientation Compensation Algorithm for Distance Measurement

Low-cost radio-based distance measurement devices, (like) such as ultrawideband (UWB) and Zigbee, are limited by transmission power and environmental noise interference, resulting in bias due to variations in the angles between paired devices. Considering the impact of unmanned aerial vehicle (UAV) attitude on this measurement bias, a compensation model using Gaussian process regression (GPR) is established first, by mapping the two pseudo-Euler angles under the defined p-frame and the corresponding measurement bias. We take the UAV swarms as an example to deduce the relationship between the UAV quaternion and two pseudo-Euler angles, including the mean and the variance. Based on the GPR compensation model, a GPR-based multitarget orientation compensation algorithm (GPRMOCA) is proposed to solve the measurement bias problem in multitarget positioning. The algorithm allows us to calculate the corresponding compensation and its uncertainty for distance measurement between the target and each anchor. Importantly, this algorithm is not limited to UAVs but applies to various scenarios, including indoor positioning. Four UAV experiments verify the effectiveness of the proposed GPRMOCA algorithm. We find that there are two bias peaks when the device rotates 360°, and the proposed algorithm has a better effect for correcting the deviation of the peak.