Zhang Lifei, Viktorovich Proletarsky Andrey, Selezneva Maria Sergeevna, Neusypin Konstantin Avenirovich
Department of Informatics and Control Systems, Bauman Moscow State Technical University, 101000 Moscow, Russia.
Sensors (Basel). 2021 Jan 17;21(2):623. doi: 10.3390/s21020623.
In this paper, a low-cost small-sized strap-down inertial navigation system (SINS)-Gyrolab GL-VG 109-is studied. When the system is installed on an unmanned vehicle and works in autonomous mode, it is difficult to determine the navigation parameters of the unmanned vehicle. Correcting the SINS information from the Global Navigation Satellite System (GNSS) can significantly increase the determination accuracy of the navigation parameters. However, this is only available when the GNSS signals are stable. A new adaptive estimation algorithm that can automatically detect, evaluate, and process the abnormal measurements is proposed in the present work. The determination of the navigation parameters can reach the third accuracy class using the proposed method. The effectiveness of the algorithm is verified by the mathematical simulation and the experimental tests (with a real SINS GL-VG 109), which are conducted in urban environments with a GNSS signal containing 15% and 40% abnormal measurements. The results show that the proposed method can significantly reduce the impact of abnormal measurements and improve the estimation accuracy.
本文研究了一种低成本小型捷联惯性导航系统（SINS）——Gyrolab GL-VG 109。当该系统安装在无人驾驶车辆上并以自主模式工作时，很难确定无人驾驶车辆的导航参数。利用全球导航卫星系统（GNSS）校正SINS信息可以显著提高导航参数的确定精度。然而，这只有在GNSS信号稳定时才可行。本文提出了一种新的自适应估计算法，该算法可以自动检测、评估和处理异常测量值。使用所提出的方法，导航参数的确定可以达到三级精度。通过数学仿真和实验测试（使用真实的SINS GL-VG 109）验证了该算法的有效性，实验测试是在城市环境中进行的，GNSS信号中包含15%和40%的异常测量值。结果表明，所提出的方法可以显著降低异常测量值的影响并提高估计精度。
