This paper develops a distributed collaborative localization algorithm based on an extended kalman filter. This algorithm incorporates Ultra-Wideband (UWB) measurements for vehicle to vehicle ranging, and shows improvements in localization accuracy where GPS typically falls short. The algorithm was first tested in a newly created open-source simulation environment that emulates various numbers of vehicles and sensors while simultaneously testing multiple localization algorithms. Predicted error distributions for various algorithms are quickly producible using the Monte-Carlo method and optimization techniques within MatLab. The simulation results were validated experimentally in an outdoor, urban environment. Improvements of localization accuracy over a typical extended kalman filter ranged from 2.9% to 9.3% over 180 meter test runs. When GPS was denied, these improvements increased up to 83.3% over a standard kalman filter. In both simulation and experimentally, the DCL algorithm was shown to be a good approximation of a full state filter, while reducing required communication between vehicles. These results are promising in showing the efficacy of adding UWB ranging sensors to cars for collaborative and landmark localization, especially in GPS-denied environments. In the future, additional moving vehicles with additional tags will be tested in other challenging GPS denied environments.

翻译：本文提出了一种基于扩展卡尔曼滤波的分布式协作定位算法。该算法融合了超宽带（UWB）测距技术以实现车辆间测距，并在全球定位系统（GPS）信号欠佳的场景中显著提升了定位精度。首先，在一个新开发的开源仿真环境中对该算法进行了测试，该环境可模拟不同数量的车辆与传感器，并同时评估多种定位算法。通过蒙特卡洛方法与MatLab中的优化技术，可快速生成各算法的预测误差分布。仿真结果在室外城市环境中得到了实验验证。在180米测试行程中，与标准扩展卡尔曼滤波相比，定位精度提升了2.9%至9.3%；在GPS信号缺失条件下，精度提升幅度最高达83.3%，显著优于标准卡尔曼滤波。仿真与实验均表明，该分布式协作定位（DCL）算法在减少车辆间通信需求的同时，能够良好地逼近全状态滤波器。这些结果有力地证明了在车辆上配置UWB测距传感器对协作定位与地标定位的有效性，特别是在GPS信号缺失环境中。未来，将在更具挑战性的GPS缺失场景中，对搭载更多标签的移动车辆进行进一步测试。