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An Accurate and Fault-Tolerant Target Positioning System for Buildings Using Laser Rangefinders and Low-Cost MEMS-Based MARG Sensors

机译:使用激光测距仪和低成本基于MEMS的MARG传感器为建筑物提供精确且容错的目标定位系统

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Target positioning systems based on MEMS gyros and laser rangefinders (LRs) have extensive prospects due to their advantages of low cost, small size and easy realization. The target positioning accuracy is mainly determined by the LR’s attitude derived by the gyros. However, the attitude error is large due to the inherent noises from isolated MEMS gyros. In this paper, both accelerometer/magnetometer and LR attitude aiding systems are introduced to aid MEMS gyros. A no-reset Federated Kalman Filter (FKF) is employed, which consists of two local Kalman Filters (KF) and a Master Filter (MF). The local KFs are designed by using the Direction Cosine Matrix (DCM)-based dynamic equations and the measurements from the two aiding systems. The KFs can estimate the attitude simultaneously to limit the attitude errors resulting from the gyros. Then, the MF fuses the redundant attitude estimates to yield globally optimal estimates. Simulation and experimental results demonstrate that the FKF-based system can improve the target positioning accuracy effectively and allow for good fault-tolerant capability.
机译:基于MEMS陀螺仪和激光测距仪(LR)的目标定位系统具有低成本,小尺寸和易于实现的优点,因此具有广阔的前景。目标定位精度主要由陀螺仪得出的LR姿态决定。但是,由于来自隔离的MEMS陀螺仪的固有噪声,姿态误差很大。本文介绍了加速度计/磁力计和LR姿态辅助系统,以帮助MEMS陀螺仪。采用无复位联邦卡尔曼滤波器(FKF),它由两个局部卡尔曼滤波器(KF)和一个主滤波器(MF)组成。通过使用基于方向余弦矩阵(DCM)的动态方程式和两个辅助系统的测量值来设计局部KF。 KF可以同时估计姿态,以限制由陀螺仪引起的姿态误差。然后,MF将多余的姿态估计值融合起来以产生全局最优估计值。仿真和实验结果表明,基于FKF的系统可以有效地提高目标定位精度,并具有良好的容错能力。

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