Relative Localization for UAV-UGV Based on Correlation of Bearing and Distance

Jia Guo, Kang Hu, Jinting Liu
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Abstract

To ensure reliable localization for Unmanned Aerial Vehicles(UAVs) in the presence of uncertain speed of the Unmanned Ground Vehicle (UGV), this study examines the relative localization problem of UAV-UGV using distance and bearing measurements. A Correlation of Bearing and Distance-based Relative Localization (CBDRL) algorithm is proposed in this paper under this scenario. The estimation of altitude, distance, and angle are simplified into a representation of the relative positioning between the UAV and UGV. The relative height difference is measured using the barometer in the algorithm. To determine the relative distance, Time of Arrival (TOA) ranging and Ultra Wide Band (UWB) communication are utilized. The relative direction measurement is then determined using the correlations of bearing and distance. We integrate these observations with height, direction, and distance data in an Extended Kalman Filter(EKF) to provide accurate and reliable relative position estimates that allow the UAV to track the target. The simulation results indicate that the CBDRL method developed in this study is superior to previous relative localization algorithms that rely on multi-sensor fusion, and can significantly enhance the accuracy of UAV positioning provided that range and angle measurements are precise enough.
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基于方位与距离相关性的无人机- ugv相对定位
为了保证无人地面飞行器(UGV)在速度不确定的情况下的可靠定位,本研究利用距离和方位测量对无人机-UGV的相对定位问题进行了研究。针对这种情况,本文提出了一种基于方位与距离相关的相对定位算法(CBDRL)。将高度、距离和角度的估计简化为无人机和UGV之间相对定位的表示。算法中采用气压计测量相对高差。为了确定相对距离,使用了到达时间(TOA)测距和超宽带(UWB)通信。然后利用方位和距离的相关性确定相对方向测量。我们将这些观测结果与高度、方向和距离数据整合到扩展卡尔曼滤波器(EKF)中,以提供准确可靠的相对位置估计,使无人机能够跟踪目标。仿真结果表明,本研究开发的CBDRL方法优于以往依赖多传感器融合的相对定位算法,在距离和角度测量足够精确的情况下,可以显著提高无人机定位精度。
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