BluePrint: BLE Positioning Algorithm Based on NUFO Detection

Ho, Yik Him; Chan, Henry C. B.

doi:10.1109/glocom.2017.8254483

Cited by 8 publications

(3 citation statements)

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“…Likewise, most fingerprinting approaches use a variety of ambient features and need dense infrastructure for higher positioning accuracy [ 25 , 26 ]. Recently, some fingerprinting methods focus on employing the received signal strength (RSS) of iBeacon modules as a surrounding feature [ 5 , 27 ]. Due to the usage of iBeacon based on BLE, they are more energy-efficient compared with the existing Bluetooth and WiFi modules.…”

Section: Related Workmentioning

confidence: 99%

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Indoor Pedestrian Localization Using iBeacon and Improved Kalman Filter

Sung

Lee

Kim

2018

Sensors

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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.

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Section: Related Workmentioning

confidence: 99%

“…RSS fingerprinting is a kind of the localization method using the received signal strength (RSS) from the radio beacons such as WiFi access points (APs) [ 3 , 4 ], Bluetooth devices [ 5 ], and cellular telephone towers [ 6 ]. This approach consists of two phases: offline and online.…”

Section: Introductionmentioning

confidence: 99%

Indoor Pedestrian Localization Using iBeacon and Improved Kalman Filter

Sung

Lee

Kim

2018

Sensors

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“…A fingerprint-based method is one of the positioning approaches employing RSS values obtained by the wireless infrastructures, including cell towers [6], fixed Bluetooth modules [7,8,9], and 802.11 access points (APs) [10,11,12]. The fingerprinting approach is performed through offline and online phases.…”

Section: Introductionmentioning

confidence: 99%

Pedestrian Positioning Using a Double-Stacked Particle Filter in Indoor Wireless Networks

Sung

Lee

Kim

2019

Sensors

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The indoor pedestrian positioning methods are affected by substantial bias and errors because of the use of cheap microelectromechanical systems (MEMS) devices (e.g., gyroscope and accelerometer) and the users’ movements. Moreover, because radio-frequency (RF) signal values are changed drastically due to multipath fading and obstruction, the performance of RF-based localization systems may deteriorate in practice. To deal with this problem, various indoor localization methods that integrate the positional information gained from received signal strength (RSS) fingerprinting scheme and the motion of the user inferred by dead reckoning (DR) approach via Bayes filters have been suggested to accomplish more accurate localization results indoors. Among the Bayes filters, while the particle filter (PF) can offer the most accurate positioning performance, it may require substantial computation time due to use of many samples (particles) for high positioning accuracy. This paper introduces a pedestrian localization scheme performed on a mobile phone that leverages the RSS fingerprint-based method, dead reckoning (DR), and improved PF called a double-stacked particle filter (DSPF) in indoor environments. As a key element of our system, the DSPF algorithm is employed to correct the position of the user by fusing noisy location data gained by the RSS fingerprinting and DR schemes. By estimating the position of the user through the proposal distribution and target distribution obtained from multiple measurements, the DSPF method can offer better localization results compared to the Kalman filtering-based methods, and it can achieve competitive localization accuracy compared with PF while offering higher computational efficiency than PF. Experimental results demonstrate that the DSPF algorithm can achieve accurate and reliable localization with higher efficiency in computational cost compared with PF in indoor environments.

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