Positioning Method Without GNSS for Unmanned Systems Based on Fusion of IMU, TOA and AOA

Abstract

AbstractIn this paper, the inertial navigation system (INS) is combined with the wireless signal to meet the positioning requirements of the Unmanned Aerial Vehicle (UAV) system under the condition of weak or no GNSS conditions. The inertial measurement unit (IMU) is used to obtain the acceleration and angular velocity of the UAV. Meanwhile, time of arrival (TOA) and angle of arrival (AOA) information are used to measure the distance and Angle from the target to the anchors, which is a UAV with known coordinates. We established the state transition model and observation model based on the measurement information, and the estimation is performed by the extended Kalman Filter (EKF). The experimental results show that the introduction of TOA and AOA measurement information can significantly improve the whole error. The introduction of new information on the improvement of the error is remarkable, but the optimization is not as great as the increase of the number of anchors, add more measurement information not only raise the computations, may also polluted the precision because of the introduction of the new error. AOA error has a great influence on long-term results, while TOA error has a great influence on short-term results, whether it is with fewer anchors or with more anchors. This is mainly related to the topology of system. In the deficiency of anchors, we can consider introducing new information to improve the accuracy, while in the case of sufficient anchors, we need to consider a small amount of measurement information. For long-term, AOA information should be avoided as far as possible, while for short-term, AOA should be introduced to alleviate the effect of error. Therefore, different positioning methods should be adopted for different task requirements.KeywordsPositioningInertial measurement unitTime of arrivalAngle of arrivalExtended Kalman filterUnmanned aerial vehicle