Development of the iBeacon’s Positioning Algorithm for Indoor Scenarios

Andrushchak, Volodymyr; Maksymyuk, Taras; Klymash, Mykhailo; Ageyev, Dmytro

doi:10.1109/infocommst.2018.8632075

Cited by 18 publications

(10 citation statements)

References 13 publications

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“…After tuning, we obtain the optimal thresholds for each experimenter. 1e 5 , 3.3e 4 and 5e 4 are respectively the optimal thresholds to the No.1 experimenter (N1er), the No.2 experimenter (N2er) and the No.3 experimenter (N3er).…”

Section: ) Zvi Detection Resultsmentioning

confidence: 99%

“…After tuning, we obtain the optimal thresholds for each locomotion. 7e 5 , 1.4e 5 and 8.6e 4 are the optimal thresholds for LM1, LM2 and LM3.…”

Section: ) Zvi Detection Resultsmentioning

confidence: 99%

“…Indoor navigation needs other technical supports. The infrastructure-dependent localization systems, such as Wi-Fi, UWB, GSM and iBeacon, can provide accurate navigation information by the arrangement of the indoor environments in advance [2][3][4]. These systems are inflexible in real usages, and thus cannot realize autonomous navigation for users in new environments.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Adaptive Zero-Velocity Detector for Inertial Pedestrian Navigation Based on Optimal Interval Estimation

Chen

Pan

et al. 2020

IEEE Access

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In the foot-mounted inertial pedestrian navigation system, the zero-velocity update (ZUPT) algorithm is an efficient way to bound the inertial error propagation. Therefore, a reliable and accurate zerovelocity detector (ZVD) that adapts to all kinds of locomotion and scenarios plays a vital role in achieving high-precision and long-term pedestrian navigation. The classical threshold-based ZVDs are susceptible to failures during dynamic locomotion due to the fixed threshold. Recent machine-learning-based ZVDs need a huge amount of data to support the model training and their generalization is limited in new testing scenarios. In this paper, we propose a novel adaptive ZVD using the optimal interval estimation. Two filters are used to process the angular rate, aiming at determining a gait cycle. In a gait cycle, the acceleration is mapped to the search space by a special convex function. Based on the features of the data in the search space, a zero-velocity benchmark is calculated for the following interval estimation. The zero-velocity benchmark and the hierarchical iterative search are used to estimate the optimal zero-velocity interval (ZVI). The experiments demonstrate the effectiveness and adaptability of this novel ZVD.

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Section: ) Zvi Detection Resultsmentioning

confidence: 99%

“…After tuning, we obtain the optimal thresholds for each locomotion. 7e 5 , 1.4e 5 and 8.6e 4 are the optimal thresholds for LM1, LM2 and LM3.…”

Section: ) Zvi Detection Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel Adaptive Zero-Velocity Detector for Inertial Pedestrian Navigation Based on Optimal Interval Estimation

Chen

Pan

et al. 2020

IEEE Access

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“…However, two BLE based protocols, iBeacons (by Apple Inc.) and Eddystone (by Google, Inc.), have been designed for context-aware proximity services. From the messages received from the beacon and from the RSSI, users are classified into three range regions (immediate, near, far, or unknown) [64]. Based on the user's proximity to any beacon, the mobile application triggers an action (e.g.…”

Section: Bluetoothmentioning

confidence: 99%

Room-Level Localization System Based on LoRa Backscatters

2021

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The aim of this paper is to propose a novel room-level localization approach to locate LoRa backscatter devices, which can be easily embedded into wearable devices or smartphones. The advantages of this system lie in its series of low-cost, low-power, low-complexity and long-range features. LoRa backscattering operates by alternatively connecting an antenna, through a switch, to two loads with high reflection coefficients and opposite phase. The result is a frequency shift of the LoRa incident signal equal to the backscatter switching frequency. The localization system comprises several LoRa receivers distributed among the rooms, a LoRa transmitter located at a central point and the backscatter device, which is carried or worn by a subject. The position of the LoRa backscatter device can be determined by comparing the received signal strength between all receivers. In order to improve the accuracy of the system, different machine learning classifiers were compared. System performance was tested in a real-life scenario, achieving an accuracy up to 89.7% using linear discriminant analysis (LDA). INDEX TERMS Backscatter communications, LoRa, localization, wireless sensor networks, RFID, zeropower sensor, internet of things (IoT).

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“…The developments of location-based mobile services and the Internet of Things urgently need stable and precise indoor positioning technologies [ 1 ]. As the widely deployed global positioning system (GPS) has poor coverage and accuracy in the indoor environment, indoor positioning systems (IPS) that employ alternative radio frequency (RF) technologies (e.g., Bluetooth, RFID, iBeacon, Wi-Fi, and near-field communication [ 2 , 3 , 4 ]) have been investigated. However, the RF-based IPS (e.g., 1–3/5–15/0.1–0.3 m with Bluetooth/Wi-Fi/UWB, respectively [ 5 ]) can be largely affected by electromagnetic interference and multipath effect in a congested environment [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Demonstration of Three-Dimensional Indoor Visible Light Positioning with Multiple Photodiodes and Reinforcement Learning

Zhang

Chen

Zeng

et al. 2020

Sensors

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To provide high-quality location-based services in the era of the Internet of Things, visible light positioning (VLP) is considered a promising technology for indoor positioning. In this paper, we study a multi-photodiodes (multi-PDs) three-dimensional (3D) indoor VLP system enhanced by reinforcement learning (RL), which can realize accurate positioning in the 3D space without any off-line training. The basic 3D positioning model is introduced, where without height information of the receiver, the initial height value is first estimated by exploring its relationship with the received signal strength (RSS), and then, the coordinates of the other two dimensions (i.e., X and Y in the horizontal plane) are calculated via trilateration based on the RSS. Two different RL processes, namely RL1 and RL2, are devised to form two methods that further improve horizontal and vertical positioning accuracy, respectively. A combination of RL1 and RL2 as the third proposed method enhances the overall 3D positioning accuracy. The positioning performance of the four presented 3D positioning methods, including the basic model without RL (i.e., Benchmark) and three RL based methods that run on top of the basic model, is evaluated experimentally. Experimental results verify that obviously higher 3D positioning accuracy is achieved by implementing any proposed RL based methods compared with the benchmark. The best performance is obtained when using the third RL based method that runs RL2 and RL1 sequentially. For the testbed that emulates a typical office environment with a height difference between the receiver and the transmitter ranging from 140 cm to 200 cm, an average 3D positioning error of 2.6 cm is reached by the best RL method, demonstrating at least 20% improvement compared to the basic model without performing RL.

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Development of the iBeacon’s Positioning Algorithm for Indoor Scenarios

Cited by 18 publications

References 13 publications

A Novel Adaptive Zero-Velocity Detector for Inertial Pedestrian Navigation Based on Optimal Interval Estimation

A Novel Adaptive Zero-Velocity Detector for Inertial Pedestrian Navigation Based on Optimal Interval Estimation

Room-Level Localization System Based on LoRa Backscatters

Demonstration of Three-Dimensional Indoor Visible Light Positioning with Multiple Photodiodes and Reinforcement Learning

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