An indoor localization system based on 3D magnetic fingerprints for smart buildings

Moreno, María Ángeles Esteban; Skarmeta, Antonio F.

doi:10.1109/rivf.2015.7049897

Cited by 11 publications

(4 citation statements)

References 27 publications

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“…Accuracy for magnetic fingerprint-based positioning can be enhanced with several alternate strategies. For example authors in [24] propose the use of magnetic landmarks to improve the localization accuracy. The database is made using magnetic x, y, and z components of the magnetic field which is then clustered using the expectation-maximization algorithm.…”

Section: Overview Of Magnetic Field and Magnetic Positioning Approachesmentioning

confidence: 99%

Enhancing Performance of Magnetic Field Based Indoor Localization Using Magnetic Patterns from Multiple Smartphones

Ashraf

Hur

Park

2020

Sensors

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Wide expansion of smartphones triggered a rapid demand for precise localization that can meet the requirements of location-based services. Although the global positioning system is widely used for outdoor positioning, it cannot provide the same accuracy for the indoor. As a result, many alternative indoor positioning technologies like Wi-Fi, Bluetooth Low Energy (BLE), and geomagnetic field localization have been investigated during the last few years. Today smartphones possess a rich variety of embedded sensors like accelerometer, gyroscope, and magnetometer that can facilitate estimating the current location of the user. Traditional geomagnetic field-based fingerprint localization, although it shows promising results, it is limited by the fact that various smartphones have embedded magnetic sensors from different manufacturers and the magnetic field strength that is measured from these smartphones vary significantly. Consequently, the localization performance from various smartphones is different even when the same localization approach is used. So devising an approach that can provide similar performance with various smartphones is a big challenge. Contrary to previous works that build the fingerprint database from the geomagnetic field data of a single smartphone, this study proposes using the geomagnetic field data collected from multiple smartphones to make the geomagnetic field pattern (MP) database. Many experiments are carried out to analyze the performance of the proposed approach with various smartphones. Additionally, a lightweight threshold technique is proposed that can detect user motion using the acceleration data. Results demonstrate that the localization performance for four different smartphones is almost identical when tested with the database made using the magnetic field data from multiple smartphones than that of which considers the magnetic field data from only one smartphone. Moreover, the performance comparison with previous research indicates that the overall performance of smartphones is improved.

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Section: Overview Of Magnetic Field and Magnetic Positioning Approachesmentioning

confidence: 99%

Enhancing Performance of Magnetic Field Based Indoor Localization Using Magnetic Patterns from Multiple Smartphones

Ashraf

Hur

Park

2020

Sensors

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“…The positioning error of this method is within 5 m, but the amount of calculation required for position estimation is large and is resource intensive. To improve magnetic field positioning accuracy, Moreno et al [27] used magnetic landmarks to improve positioning accuracy. The obtained magnetic field x, y, z three-axis data are stored as fingerprint data in the fingerprint database, and then clustered using the expectation maximization algorithm.…”

Section: Related Workmentioning

confidence: 99%

Improving Fingerprint Indoor Localization Using Convolutional Neural Networks

Sun

Wei

et al. 2020

IEEE Access

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Two obstacles lie in the traditional Signal Strength Fingerprint Positioning method. Initially, the algorithm cannot converge quickly and accurately due to massive data generated by large indoor environment. Secondly, it is difficult to determine a specific floor in a building using the received Signal Strength(RSS). This paper proposes a method, which uses convolutional neural network (CNN) to classify the floor and location of Bluetooth RSS as well as magnetic field data to calculate the final coordinates, could apply Fingerprint Positioning into indoor environment with large areas and multiply floors. The method involves converting the collected Bluetooth RSS into the "fingerprint image" required for calculation and establishing the CNN for classification training. Subsequently, the real-time Bluetooth RSS are imported into the CNN to classify the floor and determine the transmitters' location. Additionally, the observer's coordinates are matched using the magnetic field data. Our experiments suggested that the proposed method can classify floors and transmitters' locations with predictable bunds of 0.9667 and 0.9333, respectively. At the same time, the average positioning error is less than 1.2 m, which is 43.32% and 44.67% higher than the traditional Bluetooth and magnetic field fingerprint positioning. The accuracy of dynamic positioning is also within 1.55 meters.

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“…Moreno and Skarmeta propose a magnetic field strength–based indoor localization technique, which utilizes the x, y, and z components of the magnetic field. The magnetic fingerprinting database is prepared during the off‐line phase.…”

Section: Related Workmentioning

confidence: 99%

GUIDE: Smartphone sensors‐based pedestrian indoor localization with heterogeneous devices

Ashraf

Hur

Shafiq

et al. 2019

Int J Communication

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Summary A plethora of indoor localization systems based on Wi‐Fi, radio frequency chips, ultra‐wide‐band, and bluetooth have been proposed, yet these systems do not work when the infrastructure is absent. On the other hand, infrastructure less systems benefit mostly from off‐the‐shelf smartphone sensors and do not need additional hardware. This study shows a similar indoor localization approach which turns smartphone built‐in sensors to good account. We take advantage of magnetic field strength fingerprinting approach to localize a pedestrian indoor. In addition, accelerometer and gyroscope sensors are utilized to find the pedestrian's traveled distance and heading estimation, respectively. Our aim is to solve the problem of device dependence by devising an approach that can perform localization using various smartphones in a similar fashion. We make the use of patterns of magnetic field strength to formulate the fingerprint database to achieve this goal. This approach solves two problems: need to update the database periodically and device dependence. We conduct experiments using Samsung Galaxy S8 and LG G6 for five different buildings with different dimensions in Yeungnam University, Republic of Korea. The evaluation is performed by following three different path geometries inside the buildings. The results show that the proposed localization approach can potentially be used for indoor localization with heterogeneous devices. The errors for path 1 and path 2 are very similar, however, localization error for path 3 is comparatively higher because of the complexity of the path 3. The mean and median errors for Galaxy S8 are 1.37 and 0.88 m while for LG G6, these are 1.84 and 1.21 m, respectively, while considering all buildings and all paths followed during the experiment. Overall, the proposed approach can potentially localize a pedestrian within 1.21 m at 50% and within 1.93 m at 75%, irrespective of the device used for localization. The performance of the proposed approach is compared with the K nearest neighbor (KNN) for evaluation. The proposed approach outperforms the KNN

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An indoor localization system based on 3D magnetic fingerprints for smart buildings

Cited by 11 publications

References 27 publications

Enhancing Performance of Magnetic Field Based Indoor Localization Using Magnetic Patterns from Multiple Smartphones

Enhancing Performance of Magnetic Field Based Indoor Localization Using Magnetic Patterns from Multiple Smartphones

Improving Fingerprint Indoor Localization Using Convolutional Neural Networks

GUIDE: Smartphone sensors‐based pedestrian indoor localization with heterogeneous devices

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