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Performance Analysis of Medium Accuracy FOG-Based IMU Integrated with Optical Odometer for Land Vehicle Navigation

机译:基于中学迷路的陆地车辆导航光学里程表集成的性能分析

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摘要

The aim of this work is to research the feasibility of using optical odometer as the aided sensor for accuracy improvement of medium accuracy (FOG)-based IMU for land vehicle navigation. Usually GNSS is integrated with low cost SINS (strapdown inertial navigation systems) for land vehicle navigation but GNSS is not always reliable in land vehicle applications. The focus is on analysis of position and velocity accuracy of SINS/Odometer integration using close loop kalman filter. Integrated navigation algorithm for vehicle states estimation and correction have been designed and implemented. The measurement error model for odometer in navigation frame is developed for Kalman filter implementation. As the prior knowledge of measurement noise which represents the stochastic properties of odometer is not exactly known, so an adaptive Kalman filter (AKF) is also proposed for online estimation of the measurement noise matrix in order to improve the accuracy. For the performance analysis of the designed system, field test is carried out and results show that the accuracy of the medium accuracy fiber optics gyro (FOG)-based SINS is improved and the systems is capable for land vehicle navigation application.
机译:这项工作的目的是研究使用光学里程表作为辅助传感器的可行性,用于准确提高中等精度(雾)的陆地车辆导航的IMU。通常,GNSS与用于陆地车辆导航的低成本血管(塞子惯性导航系统)集成,但GNSS在陆地车辆应用中并不总是可靠的。使用近环卡尔曼滤波器分析SINS / AGETOMETS集成的位置和速度精度分析。设计和实现了用于车辆状态估计和校正的集成导航算法。为卡尔曼滤波器实现开发了导航框架测量误差模型。作为表示测量噪声的测量噪声的先验知识,该测量噪声的内部特性是不确定的,因此还提出了一种自适应卡尔曼滤波器(AKF)以用于测量噪声矩阵的在线估计以提高精度。对于设计系统的性能分析,进行了现场测试,结果表明,基于中等精度光纤陀螺仪(雾)的准确性得到改善,系统能够进行陆地车辆导航应用。

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