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.
Highlights
Location-based services (LBS), which are accessible from mobile devices, have become increasingly important in recent years
It is assumed that the user’s mode uses the specific corners and intersections as to further correct the walking heading for the Pedestrian Dead Reckoning (PDR) update is “locked” to a specific value according to the indoor map
This paper presents a two-layer extended Kalman Filter (EKF)/Auxiliary Value Particle Filter (AVPF) structure algorithm to integrate micro-electromechanical system (MEMS)/Wi-Fi/Map integrated indoor LBS applications
Summary
Location-based services (LBS), which are accessible from mobile devices, have become increasingly important in recent years. When compared with the RF-based positioning method, the non-RF-based positioning methods have the advantage of being low-cost and self-contained They do not require effort to install and maintain infrastructure, which must be considered carefully during the design and implementation of commercially used LBS systems [7]. Different from MM, MA does not directly used to project the pedestrian’s position; MA utilizes map information to constrain the estimated solution through wall cross method. Both MM and MA have their benefits. The main challenge for commercial LBS systems is to minimize the costs and use all the measurements/information effectively to provide a real-time, continuous, reliable positioning solution. The acronyms used along this work can be found right after Section 4
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