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An Unconventional Full Tightly-Coupled Multi-Sensor Integration for Kinematic Positioning and Navigation

机译:用于运动定位和导航的非常规全紧密耦合的多传感器集成

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摘要

Conventionally, all of the sensors, except the IMUs, function as aiding sensors in the multisensor integrated kinematic positioning and navigation. In this way, the IMU measurements are only used in free inertial navigation calculation, not through measurement update in Kalman filter (KF) between two adjacent aiding measurement epochs. This paper strives for a novel structure of IMU/GNSS integration KF, which deploys a kinematic trajectory model as the core of the KF system model and utilizes all of the measurements, inclusive of the ones from IMUs, through measurement updates. This novel multisensor integration strategy takes advantage of modern computing technology and well advances the realization of Kaman filter for a better utilization of the IMU measurements, especially either with low-cost IMUs or in poor GNSS and/or GNSS denied environment. Moreover, one no longer needs to distinguish between the core sensors and the aiding sensors. The conceptual comparison with the conventional error-state and error measurement based inertial navigation integration shows its advantages. The results from real road tests along with discussions are also presented.
机译:传统上,除IMU之外的所有传感器,功能在多传感器集成运动定位和导航中的辅助传感器。以这种方式,IMU测量仅用于自由惯性导航计算,而不是通过在两个相邻的Aidide测量时期之间的卡尔曼滤波器(KF)中的测量更新。本文争取了IMU / GNSS集成KF的新颖结构,该模型将运动轨迹模型部署为KF系统模型的核心,并通过测量更新来利用所有测量,包括来自IMU的所有测量。这种新颖的多传感器集成策略利用了现代计算技术,并且良好地推进了Kaman滤波器的实现,以便更好地利用IMU测量,尤其是低成本IMU或贫瘠的GNSS和/或GNSS否认环境。此外,不再需要区分核心传感器和辅助传感器。与传统的误差状态和基于误差测量的惯性导航集成的概念比较显示了其优点。还提出了真正的道路测试的结果以及讨论。

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