A Protocol-Channel-Based Indoor Positioning Performance Study for Bluetooth Low Energy

Blasio, Gabriel de; Quesada-Arencibia, Alexis; García, Carmelo R.; Rodríguez-Rodríguez, José Carlos; Moreno-Díaz, Roberto

doi:10.1109/access.2018.2837497

Cited by 39 publications

(27 citation statements)

References 20 publications

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“…are the normalized gyroscope raw data and the corrected gyroscope data, respectively; K P and K I are the error control items, which are 2.0 and 0.001 based on our test, respectively. After getting the corrected gyroscope data, we should substitute ω into the quaternion differential equation and solve this equation by using the first-order Runge-Kutta method [32] to obtain the quaternion update equation as Equation (6). The quaternion values can be calculated by this equation:…”

Section: Heading Angle Estimationmentioning

confidence: 99%

“…However, in the indoor environment, because the GNSS signal is blocked by humans, buildings and many other factors, it becomes so weak that the positioning accuracy will be greatly reduced. In order to solve indoor positioning problems, many kinds of method have been studied, such as Wi-Fi positioning [3,4], Bluetooth positioning [5,6], ultra-wideband (UWB) [7,8], radio frequency identification (RFID) [9], infrared [10], ZigBee [11], etc. Every positioning method is capable of providing an indoor location service for people, especially the UWB technology which can reach sub-meter accuracy in the indoor open areas [8].…”

Section: Introductionmentioning

confidence: 99%

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

Sun

Wang

et al. 2020

Applied Sciences

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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).

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Section: Heading Angle Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Sun

Wang

et al. 2020

Applied Sciences

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“…The current indoor positioning technology is in a stage of rapid development. Researchers proposed a number of mature indoor positioning technologies, such as iBeacon Bluetooth positioning technology [11][12][13], Wi-Fi indoor positioning technology [14][15][16][17], and base station indoor positioning technology [18][19][20]. However, there can be application limitations to different indoor positioning technologies according to their positioning performance, and there is no universal indoor positioning technology that meets all the requirements of the current indoor positioning services.…”

Section: Augmented Reality and Indoor Positioningmentioning

confidence: 99%

Development of Augmented Reality Indoor Navigation System based on Enhanced A* Algorithm

Yao¹,

Park²,

An³

et al. 2019

KSII TIIS

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Nowadays modern cities develop in a very rapid speed. Buildings become larger than ever and the interior structures of the buildings are even more complex. This drives a high demand for precise and accurate indoor navigation systems. Although the existing commercially available 2D indoor navigation system can help users quickly find the best path to their destination, it does not intuitively guide users to their destination. In contrast, an indoor navigation system combined with augmented reality technology can efficiently guide the user to the destination in real time. Such practical applications still have various problems like position accuracy, position drift, and calculation delay, which causes errors in the navigation route and result in navigation failure. During the navigation process, the large computation load and frequent correction of the displayed paths can be a huge burden for the terminal device. Therefore, the navigation algorithm and navigation logic need to be improved in the practical applications. This paper proposes an improved navigation algorithm and navigation logic to solve the problems, creating a more accurate and effective augmented reality indoor navigation system.

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“…However, such methods are highly susceptible to drift in gyroscope readings and fluctuations in a magnetic field in a room. Infrastructure-based positioning methods require preinstalled transmitters such as Bluetooth [12], [13], Infra-Red (IR) [14], ultrasonic [15], Ultra Wide Band (UWB) [16], Radio Frequency Identification (RFID) [17], Wi-Fi [18], [19]. Some studies also focused on indoor positioning using multimodal fingerprints [5] of those wireless signals.…”

Section: Introductionmentioning

confidence: 99%

Feature Fusion Using Stacked Denoising Auto-Encoder and GBDT for Wi-Fi Fingerprint-Based Indoor Positioning

Zhang

et al. 2020

IEEE Access

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It may be very difficult to receive the signals from satellite positioning systems due to the existing obstacles in indoor environment. Arising from the popularity of smart phones, Wi-Fi based indoor positioning technology has the advantages with convenient deployment and low hardware cost. In this study, we focused on indoor positioning using Wi-Fi fingerprint data that were collected in shopping malls. Due to the volatility of Wi-Fi signals and the high-dimensional sparseness of fingerprint data, we proposed a feature extraction algorithm, called joint multi-task stacked denoising auto-encoder (JMT-SDAE), aiming at reducing the dimensionality of the original fingerprint data and improving the indoor positioning performance in shopping malls. Furthermore, the features extracted by JMT-SDAE and gradient boosting decision tree (GBDT) were merged to construct a hybrid model, named as JMT-SDAE+GBDT. The experimental results based on 13 location datasets showed that the proposed feature fusion model had better positioning accuracy when compared with other existing positioners, and thus confirmed the effectiveness of our proposed feature extraction algorithm through multi-task learning. INDEX TERMS Indoor positioning, Wi-Fi fingerprint, feature fusion, stacked denoising auto-encoder, multi-task learning, gradient boosting decision tree.

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A Protocol-Channel-Based Indoor Positioning Performance Study for Bluetooth Low Energy

Cited by 39 publications

References 20 publications

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

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

Development of Augmented Reality Indoor Navigation System based on Enhanced A* Algorithm

Feature Fusion Using Stacked Denoising Auto-Encoder and GBDT for Wi-Fi Fingerprint-Based Indoor Positioning

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