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Vehicle state estimation for INS/GPS aided by sensors fusion and SCKF-based algorithm

机译：通过传感器融合和基于SCKF的算法帮助INS / GPS的车辆状态估计

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To improve the safety and stability of land vehicles, this paper explores the estimation problem for vehicle states, including lateral velocity and attitude. First, a robust sliding mode observer is introduced to improve the adaptability for uncertain inputs, especially for the varying parameters in the vehicle dynamic model and longitudinal velocity. Furthermore, theoretical studies are performed to enhance the capability of the observer. In order to mitigate errors with the integrated navigation system, sensor drift model is primarily established based on a modified Elman neural network, so as to investigate the coupling between driving motion and errors. In addition, an extended square-root cubature Kalman filter is proposed to combine measurements from different sensors, utilizing a fusion strategy, to deal with severe driving motion and state estimation problems. Finally, simulation and field tests are carried out under a variety of maneuvers and conditions. The approach is compared with existing methods and evaluated experimentally, which indicates its effectiveness in improving the accuracy of vehicle state estimation.
机译：为提高陆地车辆的安全性和稳定性，本文探讨了车辆状态的估计问题，包括横向速度和姿态。首先，引入了一种稳健的滑动模式观察者，以提高对不确定输入的适应性，特别是对于车辆动态模型和纵向速度的变化参数。此外，进行理论研究以增强观察者的能力。为了利用集成导航系统的错误，传感器漂移模型主要基于改进的ELMAN神经网络建立，以研究驱动运动和错误之间的耦合。另外，提出了一种扩展的方形根搭配卡尔曼滤波器以利用融合策略来组合来自不同传感器的测量值，以处理严重的行驶运动和状态估计问题。最后，仿真和现场测试是在各种机动和条件下进行的。将该方法与现有方法进行比较并通过实验评估，这表明其在提高车辆状态估计的准确性方面的有效性。

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