基于无气味卡尔曼滤波的室内移动定位与导航

Chun-Jung Sun, Hong-Yi Kuo, Chin E. Lin
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引用次数: 18

摘要

定位是移动车辆在室内环境中最重要的功能。移动物体的精确定位可以提供更高的机动性和更强的操作能力。室内导航面临的主要挑战是实时解决更高精度的航向和位置问题。本文设计并制造了一种低成本的MEMS硬件,通过适当的传感器来关注其加速度和方向。为了提高系统运行中的跟踪精度,增加了无线传感器网络(WSN)的辅助结构。一个传感器节点,间距约为10至20米,在小区域内实现定位和导航网络。该系统使用无气味卡尔曼滤波器(UKF)测量来自每个节点的无线电信号强度。该算法可以忽略非线性模型的线性化过程。为了获得更高的阶精度,不要求对雅可比矩阵求值。估计越精确,UKF的参数调优效果越好。该算法与MEMS硬件相结合,取得了较好的室内测试效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A sensor based indoor mobile localization and navigation using Unscented Kalman Filter
Localization is the most important function to mobile vehicle in indoor environments. The precise positioning of the mobile object can provide higher mobility with more operation capability. The main challenge for indoor navigation is to solve higher accuracy heading and position in real time. In this paper, a low-cost MEMS hardware is designed and fabricated to focus on its accelerations and orientations by appropriate sensors. An auxiliary architecture of the Wireless Sensor Network (WSN) is added to improve the tracking accuracy in system operation. A sensor node, spacing around 10 to 20 meters, is implemented as a positioning and navigation network in the small area. The proposed system measures the radio signal strength from each node using the Unscented Kalman Filter (UKF). By this algorithm, the linearization process of a nonlinear model can be neglected. The evaluation of the Jacobians is not requested to get higher order accuracy. The more accurate estimation can reach, the better parameter tuning of the UKF is observed. The proposed algorithm incorporating with MEMS hardware has lead to some good indoor test results.
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