Abstract

The reliable and accurate indoor pedestrian positioning is one of the biggest challenges for location-based systems and applications. Most pedestrian positioning systems have drift error and large bias due to low-cost inertial sensors and random motions of human being, as well as unpredictable and time-varying radio-frequency (RF) signals used for position determination. To solve this problem, many indoor positioning approaches that integrate the user’s motion estimated by dead reckoning (DR) method and the location data obtained by RSS fingerprinting through Bayesian filter, such as the Kalman filter (KF), unscented Kalman filter (UKF), and particle filter (PF), have recently been proposed to achieve higher positioning accuracy in indoor environments. Among Bayesian filtering methods, PF is the most popular integrating approach and can provide the best localization performance. However, since PF uses a large number of particles for the high performance, it can lead to considerable computational cost. This paper presents an indoor positioning system implemented on a smartphone, which uses simple dead reckoning (DR), RSS fingerprinting using iBeacon and machine learning scheme, and improved KF. The core of the system is the enhanced KF called a sigma-point Kalman particle filter (SKPF), which localize the user leveraging both the unscented transform of UKF and the weighting method of PF. The SKPF algorithm proposed in this study is used to provide the enhanced positioning accuracy by fusing positional data obtained from both DR and fingerprinting with uncertainty. The SKPF algorithm can achieve better positioning accuracy than KF and UKF and comparable performance compared to PF, and it can provide higher computational efficiency compared with PF. iBeacon in our positioning system is used for energy-efficient localization and RSS fingerprinting. We aim to design the localization scheme that can realize the high positioning accuracy, computational efficiency, and energy efficiency through the SKPF and iBeacon indoors. Empirical experiments in real environments show that the use of the SKPF algorithm and iBeacon in our indoor localization scheme can achieve very satisfactory performance in terms of localization accuracy, computational cost, and energy efficiency.

Highlights

  • The Global Positioning System (GPS) is commonly used for navigation in outdoor environments.it is not available for indoor positioning due to the obstruction of signals

  • The sigma-point Kalman particle filter (SKPF) algorithm can achieve better positioning accuracy than Kalman filter (KF) and unscented Kalman filter (UKF) and comparable performance compared to particle filter (PF), and it can provide higher computational efficiency compared with PF. iBeacon in our positioning system is used for energy-efficient localization and received signal strength (RSS) fingerprinting

  • Algorithm can achieve better positioning performance than KF and UKF and competitive performance compared to PF, and it can provide higher computational efficiency compared with PF

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Summary

Introduction

The Global Positioning System (GPS) is commonly used for navigation in outdoor environments. The SKPF algorithm proposed in this study is used to provide the enhanced positioning accuracy by integrating noisy positional information estimated by DR method and the location data obtained by RSS fingerprinting with uncertainty. Empirical results in a building show that the use of the SKPF in our indoor localization system can achieve very satisfactory performance in aspect of positioning accuracy and computational cost compared with KF, UKF, and PF It is shown in the test results that the positioning system using iBeacon receiver for the RSS fingerprinting can provide more energy-efficient localization than using WiFi module.

Related Work
System Architecture
Displacement Estimation and Heading Determination
Displacement Estimation
Heading Determination
Estimation of the Positional Measurement
Pedestrian Model
Basic Idea
SKPF-Based Localization Algorithm
Experimental Testbed
Evaluation of Positional Measurement Estimation
Positioning Accuracy
Computational Complexity and Time
Energy Consumption Evaluation
Conclusions
Full Text
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