Reliable integrated navigation system based on adaptive fuzzy federated Kalman filter for automated vehicles
Reliable integrated navigation system based on adaptive fuzzy federated Kalman filter for automated vehicles
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DOI：
10.1243/09544070jauto1281
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发表时间：
2010-03
期刊：
影响因子：
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通讯作者：
Xu Li;Weigong Zhang
中科院分区：
文献类型：
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作者：
Xu Li;Weigong Zhang
摘要 全球范围内对开发高速公路上的自动驾驶车辆越来越感兴趣。从安全驾驶的角度来看，他们的导航系统应该准确、稳健、可靠。本文提出了一种基于多传感器集成的自动车辆容错导航方法，利用所提出的自适应模糊联合卡尔曼滤波器（AF-FKF）。首先详细讨论了FKF模型，该模型融合了多个冗余传感器，包括捷联惯性导航系统、载波相位差分全球定位系统、电子罗盘、机器视觉和数字地图。提出自适应模糊FKF算法，自适应调整FKF信息共享因子，有效检测和隔离FKF中的故障传感器。为了比较容错性能，还考虑了几种传统的导航方法。仿真结果表明，所提出的组合导航方法可以检测、隔离和适应不同类型的传感器故障，包括硬故障和软故障。所提出的方法可以适应复杂情况下自动车辆的高可靠导航要求。
Abstract There is an increasing interest worldwide in developing automated vehicles on the highway. From the viewpoint of safe driving, their navigation system should be accurate, robust, and reliable. This paper presents a fault-tolerant navigation approach for automated vehicles based on multi-sensor integration utilizing a proposed adaptive fuzzy federated Kalman filter (AF-FKF). The FKF model is first discussed in detail, which fuses multiple and redundant sensors incorporating strapdown inertial navigation system, carrier phase-differential global positioning system, electronic compass, machine vision, and digital map. The adaptive fuzzy FKF algorithm is then proposed to adjust the FKF information-sharing factors adaptively, detect, and isolate the faulty sensor in the FKF effectively. In order to compare the fault-tolerant performance, several traditional navigation methods are also considered. Simulation results demonstrate that the proposed integrated navigation approach can detect, isolate, and accommodate different types of sensor failures including hard failures and soft failures. The proposed approach can adapt to highly reliable navigation requirements for automated vehicles in complex situations.