Implementation and analysis of Hybrid Wireless Indoor Positioning with iBeacon and Wi-Fi

Chiu, Ching-Chieh; Hsu, Jen-Chieh; Leu, Jenq‐Shiou

doi:10.1109/icumt.2016.7765336

Cited by 15 publications

(7 citation statements)

References 10 publications

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“…At the waypoint, the AGV was expected to move from to a next waypoint, that was computed by: for (12) At the end of the navigation, the final position of the AGV was . The dash line was the true trajectory of the AGV movement.…”

Section: A Navigation Along Linear Moving Trajectoriesmentioning

confidence: 99%

“…The true AGV position was not precisely located at because there existed measurement uncertainties in Eq. (12). The estimated AGV position was not accurate because only 10 sets of wireless measurements and Multilaterations were used for positioning at the waypoint.…”

Section: A Navigation Along Linear Moving Trajectoriesmentioning

confidence: 99%

“…On the other hand, wireless indoor positioning [12] has drawn great attentions by many researchers because the sensors for wireless distance measurements are cheaper and have faster sampling rates. However, positioning based on wireless distance measurements was less accurate due to the existence of measurement uncertainties.…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic Indoor Positioning and Navigation (PIPN) of Autonomous Ground Vehicle (AGV) Based on Wireless Measurements

2021

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Recently, Autonomous Ground Vehicles (AGV) and mobile robots have been rapidly developed in various engineering applications, such as Industry 4.0 factory and smart manufacturing. Indoor navigation was one of the most important tasks for the said mobile systems as they were often designed to move from one location to another location autonomously without contacting the surrounding objects along the moving path in a usually dynamic and complex indoor environment. There were two key steps to achieve Simultaneous Localization and Mapping (SLAM). First, indoor positioning of the mobile system based on some measurements was done. The second step was to navigate itself inside the indoor map. This was a very challenging problem because there always existed uncertainties in the measurements. It was desired to estimate the positioning errors and determine a safe moving path with high reliability. This paper presented the methodologies for wireless indoor positioning and navigation of AGV with measurement uncertainties. Two kinds of AGV moving trajectories with various design parameters were simulated: a linear trajectory and a curved one. It was found that both greater number of sensors being used for wireless measurements and greater number of measurement trials for Multilateration could effectively improve the accuracy of AGV positioning. INDEX TERMSAutonomous Ground Vehicle (AGV), Indoor Positioning and Navigation (IPN), Monte Carlo Simulations (MCS), Multilateration, Wireless Distance Measurement.

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Section: A Navigation Along Linear Moving Trajectoriesmentioning

confidence: 99%

Section: A Navigation Along Linear Moving Trajectoriesmentioning

confidence: 99%

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Probabilistic Indoor Positioning and Navigation (PIPN) of Autonomous Ground Vehicle (AGV) Based on Wireless Measurements

2021

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“…However, due to the numerous brands of Android system in the Android market and the unified Bluetooth devices from different manufacturers, iBeacon positioning research for the Android phone compared to iOS is more difficult. In recent years, the use of Android for iBeacon positioning research is increasing [13,17,18], but such research is based on a single model of Android phones, not relating to other Android phones, and the Bluetooth devices in different Android phones are very different, so solution only for a kind of Android model mobile phone is not universal.…”

Section: Introductionmentioning

confidence: 99%

Research and Application of Locating Factors Based on iBeacon Technology

Wei¹,

Wu²,

Wang³

et al. 2018

dtcse

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In view of the shortage of most current research results on the basis of iBeacon localization technology based on the ideal single static environment, the problems in positioning based on iBeacon technology are analyzed and summarized through a large number of experiments. The research problems include: the impact of the iBeacon parameter configuration on iBeacon positioning, including the effect of transmit power of the iBeacon, the configuration of the transmit interval , and the factory error of iBeacon device on the positioning of the iBeacon; iBeacon's power consuption factor analysis, including the impact of transmit power, transmission interval, temperature sensing interval, inductance interval on iBeacon power consumption; RSSI correction, including RSSI attenuation analysis and the use of window to correct the RSSI; mobile device adaptation, including the comparison of RSSI reception between different Android terminal, adaptation methods and cost. And give the solutions of part of the above problems, and achieve the campus positioning system.

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“…Other systems do not need any kind of specific infrastructure, since they compute the device position using embedded sensors like magnetometer [8], accelerometer, and gyroscope. Furthermore, several hybrid approaches have been proposed, based on complementary technologies like inertial sensors and WiFi [9] or Bluetooth and WiFi [10].…”

Section: Introductionmentioning

confidence: 99%

Towards a Novel HMI Paradigm Based on Mixed EEG and Indoor Localization Platforms

Salvaro

Kartsch

Benatti

et al. 2017

2017 New Generation of CAS (NGCAS)

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Location Based Services (LBS) have been gaining a great deal of attention thanks to their capability to enhance mobile services with location awareness. While outdoor localization is almost universally achieved via Global Positioning System (GPS), indoor localization is still challenging and a general solution is yet to be found. In a vision where wearable devices are taking over smartphones' leading role as gateway to the cyber world, new paradigms of interactive Human Machine Interfaces (iHMI) are arising. Among others, one of the most intriguing alternative iHMI is based on decoding the brain signals. Combining EEG activity data and indoor localization could dramatically improve the pervasiveness of the interaction between human, devices and environment. For these reasons, we propose a portable Hardware-Software platform that acquires brain EEG signals using a dedicated board along with position information from a cloud service. The positive results of the preliminary analysis successfully show the correlation between EEG signal and motion.Understanding that this is one of the first intents to merge these two sources of information, we intend to share publicly the ever-growing dataset to allow other researchers to investigate better the interaction between subjects and environments, and to lay the foundation of new paradigms in HMI.

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Implementation and analysis of Hybrid Wireless Indoor Positioning with iBeacon and Wi-Fi

Cited by 15 publications

References 10 publications

Probabilistic Indoor Positioning and Navigation (PIPN) of Autonomous Ground Vehicle (AGV) Based on Wireless Measurements

Probabilistic Indoor Positioning and Navigation (PIPN) of Autonomous Ground Vehicle (AGV) Based on Wireless Measurements

Research and Application of Locating Factors Based on iBeacon Technology

Towards a Novel HMI Paradigm Based on Mixed EEG and Indoor Localization Platforms

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