Measurement Characterization and Autonomous Outlier Detection and Exclusion for Ground Vehicle Navigation With Cellular Signals
Measurement Characterization and Autonomous Outlier Detection and Exclusion for Ground Vehicle Navigation With Cellular Signals
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DOI：
10.1109/tiv.2020.2991947
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发表时间：
2020-12
影响因子：
8.2
通讯作者：
Mahdi Maaref;Z. Kassas
中科院分区：
文献类型：
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作者：
Mahdi Maaref;Z. Kassas
提出了一种基于蜂窝信号的地面车辆自主测量异常点检测与排除框架。在没有全球导航卫星系统（GNSS）信号的情况下，地面车辆用蜂窝信号以紧密耦合的方式辅助其机载惯性测量单元（IMU）。首先，蜂窝伪橙的特征来自于在不同环境（开放天空、城市和城市深处）中使用地面车辆收集的广泛的作战行动。然后，开发了一个框架，该框架考虑了视距阻塞和短多径延迟引起的异常值。这些异常值在细胞伪点中引起偏差，并损害导航解决方案的完整性。实验结果评估了该框架在没有GNSS信号的情况下对地面车辆导航的有效性。结果表明，该框架能够有效地检测和排除异常点，使位置均方根误差（RMSE）降低41.5%，最大位置误差降低43.1%。
An autonomous measurement outlier detection and exclusion framework for ground vehicle navigation using cellular signals is developed. The ground vehicle aids its onboard inertial measurement unit (IMU) with cellular signals in a tightly-coupled fashion in the absence of global navigation satellite system (GNSS) signals. First, cellular pseudoranges are characterized from an extensive wardriving campaign collected with a ground vehicle in different environments: open sky, urban, and deep urban. Then, a framework is developed, which accounts for outliers due to line-of-sight blockage and short multipath delays. These outliers induce biases in cellular pseudoranges and compromise the integrity of the navigation solution. Experimental results are presented evaluating the efficacy of the proposed framework on a ground vehicle navigating in the absence of GNSS signals. The results demonstrate the proposed framework detecting and excluding outliers, reducing the position root mean squared error (RMSE) by 41.5% and the maximum position error by 43.1%.