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首页> 外文期刊>Arabian Journal for Science and Engineering >Integrated WiFi/MEMS Indoor Navigation Based on Searching Space Limiting and Self‑calibration
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Integrated WiFi/MEMS Indoor Navigation Based on Searching Space Limiting and Self‑calibration

机译：基于搜索空间限制和自校准的集成WiFi / MEMS室内导航

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摘要

Indoor navigation has been increasingly popular over the last few years. However, it still faces plenty of challenges andremains a conundrum. This paper proposes a novel improved WiFi/MEMS integration solution for indoor navigation. InWiFi fingerprinting scheme, a novel searching space limiting method is originally presented and associated with a meanfilter to improve computation efficiency and positioning accuracy, compared with the traditional weighted K-nearest neighborsmethod. In pedestrian dead-reckoning part, an attitude determination extended Kalman filter with correlated processand measurement noise is presented to obtain an accurate long-term heading and the average positioning error decreasessignificantly as a result. Furthermore, the self-calibration Kalman filter approach is introduced into indoor navigation fieldin WiFi/MEMS integration stage and a novel Kalman filter system is originally designed to fuse the information effectively.The navigation performance of the proposed WiFi/MEMS algorithm has been validated by indoor experiments, and theaverage positioning error is less than 0.6 m when the number of selected APs is optimal.
机译：在过去的几年中，室内导航越来越受欢迎。但是，它仍然面临许多挑战，仍然是一个难题。本文提出了一种用于室内导航的新型改进的WiFi / MEMS集成解决方案。在WiFi指纹识别方案中，最初提出了一种新颖的搜索空间限制方法，并将其与均值滤波器相关联，以与传统的加权K最近邻方法相比提高了计算效率和定位精度。在行人沉船部分，提出了一种具有相关过程和测量噪声的姿态确定扩展卡尔曼滤波器，以获得准确的长期航向，平均定位误差明显降低。此外，自校准卡尔曼滤波方法被引入WiFi / MEMS集成阶段的室内导航领域，并最初设计了一种新颖的Kalman滤波系统以有效地融合信息。通过室内验证了所提出的WiFi / MEMS算法的导航性能实验中，当选择的AP数量最佳时，平均定位误差小于0.6 m。

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