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首页> 外文期刊>Automation Science and Engineering, IEEE Transactions on >Robust UAV Relative Navigation With DGPS, INS, and Peer-to-Peer Radio Ranging
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Robust UAV Relative Navigation With DGPS, INS, and Peer-to-Peer Radio Ranging

机译：具有DGPS，INS和对等无线电测距功能的强大的无人机相对导航

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摘要

This paper considers the fusion of carrier-phase differential GPS (CP-DGPS), peer-to-peer ranging radios, and low-cost inertial navigation systems (INS) for the application of relative navigation of small unmanned aerial vehicles (UAVs) in close formation-flight. A novel sensor fusion algorithm is presented that incorporates locally processed tightly coupled GPS/INS-based absolute navigation solutions from each UAV in a relative navigation filter that estimates the baseline separation using integer-fixed relative CP-DGPS and a set of peer-to-peer ranging radios. The robustness of the dynamic baseline estimation performance under conditions that are typically challenging for CP-DGPS alone, such as a high occurrence of phase breaks, poor satellite visibility/geometry due to extreme UAV attitude, and heightened multipath intensity, amongst others, is evaluated using Monte Carlo simulation trials. The simulation environment developed for this work combines a UAV formation flight control simulator with a GPS constellation simulator, stochastic models of the inertial measurement unit (IMU) sensor errors, and measurement noise of the ranging radios. The sensor fusion is shown to offer improved robustness for 3-D relative positioning in terms of 3-D residual sum of squares (RSS) accuracy and increased percentage of correctly fixed phase ambiguities. Moreover, baseline estimation performance is significantly improved during periods in which differential carrier phase ambiguities are unsuccessfully fixed.
机译：本文考虑了载波相位差分GPS（CP-DGPS），对等测距无线电和低成本惯性导航系统（INS）的融合，以将小型无人飞行器（UAV）用于相对导航。近距离编队飞行。提出了一种新颖的传感器融合算法，该算法将来自每个无人机的本地处理的紧密耦合的基于GPS / INS的绝对导航解决方案合并到相对导航过滤器中，该过滤器使用整数固定的相对CP-DGPS和一组点对点估计基线间隔对等测距收音机。评估了动态基线估计性能在仅对CP-DGPS而言通常具有挑战性的条件下的稳健性，这些条件包括发生相变高，由于极端UAV姿态而导致的卫星可见性/几何形状差以及多径强度升高等。使用蒙特卡洛模拟试验。为此工作开发的模拟环境将无人机编队飞行控制模拟器与GPS星座模拟器，惯性测量单元（IMU）传感器误差的随机模型以及测距无线电的测量噪声结合在一起。传感器融合显示出在3-D残留平方和（RSS）精度方面提高了3-D相对定位的鲁棒性，并增加了正确固定相位模糊度的百分比。此外，在差分载波相位模糊度未成功固定的时间段内，基线估计性能得到了显着改善。

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