Abstract
Sensors-based and radio frequency (RF)-based indoor localization technology is one of the keys in location-based services. The IEEE 802.11-2016 introduced the Wi-Fi fine timing measurement (FTM) protocol, which provides a new approach for Wi-Fi-based indoor localization. However, Wi-Fi signals are susceptible to complex indoor environments. To improve the positioning accuracy and stability, an enhanced particle filter (PF) with two different state update strategies, a new criterion for divergence monitoring and rapid re-initialization is proposed to integrate the advantages of pedestrian dead reckoning (PDR) and Wi-Fi FTM. In addition, an adaptive tilt compensation is proposed to improve real-time heading estimation of conventional PDR, and the Wi-Fi FTM outliers are detected by displacement estimation of the PDR. The experimental results show that the proposed PF has better localization performance than the single source positioning methods in a typical indoor scenario. The accuracy of final localization is within 1 m in 86.7% of the dynamic cases and the average calculation time is less than 0.5 s when the number of particles is 2000.
Highlights
Indoor positioning methods with higher accuracy have been discussed in recent years since they can play pivotal roles in the field of artificial intelligence [1]
With different combinations of inertial measurement unit (IMU) sensors, there are many approaches to obtain heading estimates, such as quaternion based on gyroscopes [37], digital compass based on accelerometers and magnetometers [6], [10], the direction cosine matrix (DCM) [38], [39], and Extended Kalman filters (EKF) fusion [40]
The particle filter (PF) algorithm was compared with single pedestrian dead reckoning (PDR) and single fine timing measurement (FTM)-based positioning methods
Summary
Indoor positioning methods with higher accuracy have been discussed in recent years since they can play pivotal roles in the field of artificial intelligence [1]. Xu et al.: Locating Smartphones Indoors Using Built-In Sensors and Wi-Fi Ranging With an Enhanced Particle Filter posed the TOF protocol to improve the time resolution [16], which achieves a time granularity at the microsecond level; this is not sufficient for indoor localization Other technologies, such as ultra-wide band (UWB), are applied to estimate channel responses to acquire a time resolution of sub-nanosecond [17]. We propose an enhanced particle filter that fuses smartphone built-in sensors, Wi-Fi FTM ranging, and simple map constraints to achieve fast and accurate indoor localization.
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