A Smartphone Indoor Positioning System Using Hybrid Localization Technology

Gang, Hui-Seon; Pyun, Jae-Young

doi:10.3390/en12193702

Cited by 22 publications

(26 citation statements)

References 35 publications

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“…On top of this, some IPS integrate different technologies for better positioning results. For example, Reference [ 118 ] employs the magnetic field, PDR, and QR code and the BLE signals to enhance the localization estimation. While it is practical performance-wise, it is not ideal in terms of battery efficiency.…”

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

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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.

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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

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“…A hybrid positioning technique using hybrid metrics including time-of-flight (ToF) and angle-of-arrival (AoA) which are combined with the RSS fingerprinting system is presented in [ 35 ]. Another hybrid positioning method based on Bluetooth beacons, geomagnetic field, inertial measurement unit (IMU) sensors, and smartphone cameras is presented in [ 36 ]. These methods enhance the localization accuracy, but hybrid positioning techniques require additional hardware equipment which increases the communication cost.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Indoor Positioning Technique Based on a Novel Received Signal Strength Indicator Distance Prediction and Correction Model

Amr

ElAttar

Azeem

et al. 2021

Sensors

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Indoor positioning has become a very promising research topic due to the growing demand for accurate node location information for indoor environments. Nonetheless, current positioning algorithms typically present the issue of inaccurate positioning due to communication noise and interferences. In addition, most of the indoor positioning techniques require additional hardware equipment and complex algorithms to achieve high positioning accuracy. This leads to higher energy consumption and communication cost. Therefore, this paper proposes an enhanced indoor positioning technique based on a novel received signal strength indication (RSSI) distance prediction and correction model to improve the positioning accuracy of target nodes in indoor environments, with contributions including a new distance correction formula based on RSSI log-distance model, a correction factor (Beta) with a correction exponent (Sigma) for each distance between unknown node and beacon (anchor nodes) which are driven from the correction formula, and by utilizing the previous factors in the unknown node, enhanced centroid positioning algorithm is applied to calculate the final node positioning coordinates. Moreover, in this study, we used Bluetooth Low Energy (BLE) beacons to meet the principle of low energy consumption. The experimental results of the proposed enhanced centroid positioning algorithm have a significantly lower average localization error (ALE) than the currently existing algorithms. Also, the proposed technique achieves higher positioning stability than conventional methods. The proposed technique was experimentally tested for different received RSSI samples’ number to verify its feasibility in real-time. The proposed technique’s positioning accuracy is promoted by 80.97% and 67.51% at the office room and the corridor, respectively, compared with the conventional RSSI trilateration positioning technique. The proposed technique also improves localization stability by 1.64 and 2.3-fold at the office room and the corridor, respectively, compared to the traditional RSSI localization method. Finally, the proposed correction model is totally possible in real-time when the RSSI sample number is 50 or more.

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“…Meanwhile, current indoor localization solutions are employed to acquire the discussed IPS characteristics. For example, the IPS presented in [10] combines several technologies to achieve good localization accuracy; however, it is less robust/scalable and has great computational complexity. Similarly, a Gaussian process assisted fingerprinting approach is proposed in [4], where the RF signature map is predicted using a regression process trained by a few RF signature datasets.…”

Section: Introductionmentioning

confidence: 99%

Improved Smartphone-Based Indoor Localization System Using Lightweight Fingerprinting and Inertial Sensors

Subedi

Kim

et al. 2021

IEEE Access

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The existing radio frequency-based positioning approaches widely used for indoor localization-based service (LBS) are fingerprinting and trilateration and their integration with the inertial sensors-based dead reckoning system. However, these localization methods have practical limits and challenges due to unstable signal strength, the cost of offline workload, computational complexity, terminal device heterogeneity, and accumulated sensor error. We propose a smartphone-based indoor localization system using weighted Spearman's foot-rule (WSF)-based probabilistic fingerprinting for reliable smartphone localization service. This localization system adopts a real-time fingerprinting position error estimation approach realizing an adaptive extended Kalman filter (AEKF) to integrate the proposed fingerprinting localization with inertial measurement unit (IMU)-based localization. This proposed WSFbased smartphone localization uses a Gaussian process regression (GPR)-based signal prediction module to deal with fingerprinting localization's offline workload. Furthermore, the smartphone localization system's expected high computational complexity is controlled by employing a data-clustering module. The proposed WSF also employs a rank vector that helps mitigate the effect of terminal device heterogeneity. The proposed localization system is experimentally evaluated at two different representative indoor environments. Experimental results obtained by real field deployment show that the mean error is 2.06 m in an elongated hallway corridor and 3.47 m in the crowded and well-furnished wide area.

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A Smartphone Indoor Positioning System Using Hybrid Localization Technology

Cited by 22 publications

References 35 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

An Enhanced Indoor Positioning Technique Based on a Novel Received Signal Strength Indicator Distance Prediction and Correction Model

Improved Smartphone-Based Indoor Localization System Using Lightweight Fingerprinting and Inertial Sensors

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