The Technology of Crowd-Sourcing Landmarks-Assisted Smartphone in Indoor Localization

Wang, Xinxin; Qin, Danyang; Guo, Ruolin; Zhao, Min; Ma, Lin; Berhane, Teklu Merhawit

doi:10.1109/access.2020.2982283

Cited by 13 publications

(9 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This issue on fingerprinting-based localization intensifies practical limits and challenges in realizing a reliable and scalable IPS to meet the required accuracy of practical IPS. Many research works have endeavored to reduce the offline workload of fingerprinting localization [ 119 , 120 ]. Some of the approaches are the use of a self-guided robot, simultaneous localization and mapping (SLAM) [ 121 , 122 ], machine learning [ 123 , 124 ], and crowdsourcing [ 125 , 126 ].…”

Section: The Problems Of Practical Ipsmentioning

confidence: 99%

A Survey of Smartphone-Based Indoor Positioning System Using RF-Based Wireless Technologies

Subedi

Pyun

2020

Sensors

View full text Add to dashboard Cite

In recent times, social and commercial interests in location-based services (LBS) are significantly increasing due to the rise in smart devices and technologies. The global navigation satellite systems (GNSS) have long been employed for LBS to navigate and determine accurate and reliable location information in outdoor environments. However, the GNSS signals are too weak to penetrate buildings and unable to provide reliable indoor LBS. Hence, GNSS’s incompetence in the indoor environment invites extensive research and development of an indoor positioning system (IPS). Various technologies and techniques have been studied for IPS development. This paper provides an overview of the available smartphone-based indoor localization solutions that rely on radio frequency technologies. As fingerprinting localization is mostly accepted for IPS development owing to its good localization accuracy, we discuss fingerprinting localization in detail. In particular, our analysis is more focused on practical IPS that are realized using a smartphone and Wi-Fi/Bluetooth Low Energy (BLE) as a signal source. Furthermore, we elaborate on the challenges of practical IPS, the available solutions and comprehensive performance comparison, and present some future trends in IPS development.

show abstract

Section: The Problems Of Practical Ipsmentioning

confidence: 99%

A Survey of Smartphone-Based Indoor Positioning System Using RF-Based Wireless Technologies

Subedi

Pyun

2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Reference [9] proposed a problem of localization and navigation for blind people in indoor environments using processing and sensing techniques of smartphones rather than relying on external technologies. Reference [10] proposed a crowd sourced landmark indoor localization method based on accelerometer, magnetometer and gyroscope sensors of smartphones and adaptively optimized landmark algorithms are used correct the drift errors caused by sensor readings with the corresponding landmark database established by the experimental environment.…”

Section: Introductionmentioning

confidence: 99%

Indoor Positioning Algorithm Based on Reconstructed Observation Model and Particle Filter

Cao

Feng

et al. 2022

IJGI

View full text Add to dashboard Cite

In a complex indoor environment, wireless signals are affected by multiple factors such as reflection, scattering or diffuse reflection of electromagnetic waves from indoor walls and other objects, and the signal strength will fluctuate significantly. For the signal strength and the distance between the unknown nodes and the known nodes are a typical nonlinear estimation problem, and the unknown nodes cannot receive all Access Points (APs) signal strength data, this paper proposes a Particle Filter (PF) indoor position algorithm based on the Kernel Extreme Learning Machine (KELM) reconstruction observation model. Firstly, on the basis of establishing a fingerprint database of wireless signal strength and unknown node position, we use KELM to convert the fingerprint location problem into a machine learning problem and establish the mapping relationship between the location of the unknown node and the wireless signal strength, thereby refocusing construct an observation model of the indoor positioning system. Secondly, according to the measured values obtained by KELM, PF algorithm is adopted to obtain the predicted value of the unknown nodes. Thirdly, the predicted value is fused with the measured value obtained by KELM to locate the position of the unknown nodes. Moreover, a novel control strategy is proposed by introducing a reception factor to deal with the situation that unknown nodes in the system cannot receive all of the AP data, i.e., data loss occurs. This indoor positioning experimental results show that the accuracy of the method is significantly improved contrasted with commonly used PF, GP-PF and other positioning algorithms.

show abstract

“…Modern smartphones have various embedded sensors (e.g., gyroscopes, magnetometers, and accelerometers) that can be utilized for navigation and tracking. However, the use of PDR alone with inertial sensors without any correction suffers from drift error, and directional deviations [13]. Floor plan information consisting of walls and user hallways can address the PDR positioning estimation using previous knowledge of the building and user behavior [14].…”

Section: Introductionmentioning

confidence: 99%

Floor Map-Aware Particle Filtering Based Indoor Navigation System

Junoh

Subedi²,

Pyun

2021

IEEE Access

View full text Add to dashboard Cite

Smartphone-based indoor navigation systems are becoming increasingly popular in a variety of applications. However, localization accuracy has always been a challenge. The Kalman filter (KF) is a well-known estimation in the Bayesian framework, but can only deal with linear problems and Gaussian models. A particle filter (PF) is another essential estimation tool in a Bayesian system. However, a critical challenge with PF is the problem of particle degradation after resampling. To mitigate the particle degradation problem in PF, unsupervised learning based on k-means clustering is proposed in this paper. It forms clusters of similar particles based on the sum of weights. Also, we present enhancing the PF by utilizing a map constraint and k-means clustering (PFMK) and integrating Bluetooth low energy (BLE) along with pedestrian dead reckoning (PDR) for positioning. BLE and PDR-based positioning with a map constraint lead to an increase in accuracy of at least 20% compared with a traditional PF. Moreover, the proposed unsupervised k-means approach increases the accuracy by an additional 20%, whereas the overall performance of PFMK achieves a mean error of <1.5 m in the test environments.INDEX TERMS Bluetooth low energy (BLE) beacons, indoor navigation, map constraints, particle filter, unsupervised learning.

show abstract

The Technology of Crowd-Sourcing Landmarks-Assisted Smartphone in Indoor Localization

Cited by 13 publications

References 38 publications

A Survey of Smartphone-Based Indoor Positioning System Using RF-Based Wireless Technologies

A Survey of Smartphone-Based Indoor Positioning System Using RF-Based Wireless Technologies

Indoor Positioning Algorithm Based on Reconstructed Observation Model and Particle Filter

Floor Map-Aware Particle Filtering Based Indoor Navigation System

Contact Info

Product

Resources

About