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一种用于室内基于位置服务应用的地图/惯性导航系统/无线网络集成系统。

A Map/INS/Wi-Fi Integrated System for Indoor Location-Based Service Applications.

作者信息

Yu Chunyang, Lan Haiyu, Gu Fuqiang, Yu Fei, El-Sheimy Naser

机构信息

College of Automation, Harbin Engineering University, Harbin 150001, China.

Department of Geomatics, University of Calgary, Calgary, AB T2N 1N4, Canada.

出版信息

Sensors (Basel). 2017 Jun 2;17(6):1272. doi: 10.3390/s17061272.

DOI:10.3390/s17061272
PMID:28574471
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5492796/
Abstract

In this research, a new Map/INS/Wi-Fi integrated system for indoor location-based service (LBS) applications based on a cascaded Particle/Kalman filter framework structure is proposed. Two-dimension indoor map information, together with measurements from an inertial measurement unit (IMU) and Received Signal Strength Indicator (RSSI) value, are integrated for estimating positioning information. The main challenge of this research is how to make effective use of various measurements that complement each other in order to obtain an accurate, continuous, and low-cost position solution without increasing the computational burden of the system. Therefore, to eliminate the cumulative drift caused by low-cost IMU sensor errors, the ubiquitous Wi-Fi signal and non-holonomic constraints are rationally used to correct the IMU-derived navigation solution through the extended Kalman Filter (EKF). Moreover, the map-aiding method and map-matching method are innovatively combined to constrain the primary Wi-Fi/IMU-derived position through an Auxiliary Value Particle Filter (AVPF). Different sources of information are incorporated through a cascaded structure EKF/AVPF filter algorithm. Indoor tests show that the proposed method can effectively reduce the accumulation of positioning errors of a stand-alone Inertial Navigation System (INS), and provide a stable, continuous and reliable indoor location service.

摘要

本研究提出了一种基于级联粒子/卡尔曼滤波器框架结构的新型地图/惯性导航系统/无线保真（Map/INS/Wi-Fi）集成系统，用于室内基于位置的服务（LBS）应用。二维室内地图信息与来自惯性测量单元（IMU）的测量数据以及接收信号强度指示（RSSI）值相结合，用于估计定位信息。本研究的主要挑战在于如何有效利用相互补充的各种测量数据，以便在不增加系统计算负担的情况下获得准确、连续且低成本的位置解。因此，为消除低成本IMU传感器误差引起的累积漂移，通过扩展卡尔曼滤波器（EKF）合理利用无处不在的Wi-Fi信号和非完整约束来校正由IMU得出的导航解。此外，创新性地将地图辅助方法和地图匹配方法相结合，通过辅助值粒子滤波器（AVPF）来约束主要由Wi-Fi/IMU得出的位置。不同的信息源通过级联结构EKF/AVPF滤波算法进行整合。室内测试表明，所提方法能够有效减少独立惯性导航系统（INS）定位误差的累积，并提供稳定、连续且可靠的室内定位服务。

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