Floor Identification Using Magnetic Field Data with Smartphone Sensors

Ashraf, Imran; Hur, Soojung; Shafiq, Muhammad; Park, Yongwan

doi:10.3390/s19112538

Cited by 29 publications

(20 citation statements)

References 44 publications

(53 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…So the floor detection is important to determine the current position of the user. Although the magnetic field data has been reported to be used for floor detection [18], yet the pressure sensor is a reliable source for floor detection in the short run. Floor detection is performed using the following equations:…”

Section: B: Pressure Sensormentioning

confidence: 99%

The IPIN 2019 Indoor Localisation Competition—Description and Results

Potortì¹,

Park²,

Crivello³

et al. 2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

Section: B: Pressure Sensormentioning

confidence: 99%

The IPIN 2019 Indoor Localisation Competition—Description and Results

Potortì¹,

Park²,

Crivello³

et al. 2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

“…WLAN, RFID, and Bluetooth are among the RF-based techniques. Audio, visual, ultra-sonic, Infra-Red (IR), laser sensors, and magnetic field are considered as non-RF-based methods [15][16][17][18]. In this article, we mainly focus on RF-based techniques.…”

Section: Related Workmentioning

confidence: 99%

RSS-Fingerprint Dimensionality Reduction for Multiple Service Set Identifier-Based Indoor Positioning Systems

Abed

Abdel‐Qader

2019

Applied Sciences

View full text Add to dashboard Cite

Indoor positioning systems (IPS) have been recently adopted by many researchers for their broad applications in various Internet of Things (IoT) fields such as logistics, health, construction industries, and security. Received Signal Strength (RSS)-based fingerprinting approaches have been widely used for positioning inside buildings because they have a distinct advantage of low cost over other indoor positioning techniques. The signal power RSS is a function of the distance between the Mobile System (MS) and Access Point (AP), which varies due to the multipath propagation phenomenon and human body blockage. Furthermore, fingerprinting approaches have several disadvantages such as labor cost, diversity (in signals and environment), and computational cost. Eliminating redundancy by ruling out non-informative APs not only reduces the computation time, but also improves the performance of IPS. In this article, we propose a dimensionality reduction technique in a multiple service set identifier-based indoor positioning system with Multiple Service Set Identifiers (MSSIDs), which means that each AP can be configured to transmit N signals instead of one signal, to serve different kinds of clients simultaneously. Therefore, we investigated various kinds of approaches for the selection of informative APs such as spatial variance, strongest APs, and random selection. These approaches were tested using two clustering techniques including K-means and Fuzzy C-means. Performance evaluation was focused on two elements, the number of informative APs versus the accuracy of the proposed system. To assess the proposed system, real data was acquired from within the College of Engineering and Applied Sciences (CEAS) at the Western Michigan University (WMU) building. The results exhibit the superiority of fused Multiple Service Set Identifiers (MSSID) performance over the single SSID. Moreover, the results report that the proposed system achieves a positioning accuracy <0.85 m over 3000 m2, with an accumulative density function (CDF) of 88% with a distance error of 2 m.

show abstract

“…However, the magnetic data sequence will generate large fluctuations due to the device heterogeneity. Even though some methods have been studied to solve this problem, they cannot be implemented in real-time positioning [20][21][22]. What is worse is that SBMP is restricted by the trajectory and walking speed [16,23].…”

Section: Introductionmentioning

confidence: 99%

Indoor Positioning Integrating PDR/Geomagnetic Positioning Based on the Genetic-Particle Filter

Sun

Wang

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

This paper proposes a fusion indoor positioning method that integrates the pedestrian dead-reckoning (PDR) and geomagnetic positioning by using the genetic-particle filter (GPF) algorithm. In the PDR module, the Mahony complementary filter (MCF) algorithm is adopted to estimate the heading angles. To improve geomagnetic positioning accuracy and geomagnetic fingerprint specificity, the geomagnetic multi-features positioning algorithm is devised and five geomagnetic features are extracted as the single-point fingerprint by transforming the magnetic field data into the geographic coordinate system (GCS). Then, an optimization mechanism is designed by using gene mutation and the method of reconstructing a particle set to ameliorate the particle degradation problem in the GPF algorithm, which is used for fusion positioning. Several experiments are conducted to evaluate the performance of the proposed methods. The experiment results show that the average positioning error of the proposed method is 1.72 m and the root mean square error (RMSE) is 1.89 m. The positioning precision and stability are improved compared with the PDR method, geomagnetic positioning, and the fusion-positioning method based on the classic particle filter (PF).

show abstract

Floor Identification Using Magnetic Field Data with Smartphone Sensors

Cited by 29 publications

References 44 publications

The IPIN 2019 Indoor Localisation Competition—Description and Results

The IPIN 2019 Indoor Localisation Competition—Description and Results

RSS-Fingerprint Dimensionality Reduction for Multiple Service Set Identifier-Based Indoor Positioning Systems

Indoor Positioning Integrating PDR/Geomagnetic Positioning Based on the Genetic-Particle Filter

Contact Info

Product

Resources

About