Robust Local Weighted Regression for  Magnetic Map-Based Localization on Smartphone Platform

Meng, Zhibin; Wang, Mei; Wang, Enliang; Xu, Xiangyu

doi:10.4236/jcc.2017.53010

Cited by 6 publications

(5 citation statements)

References 18 publications

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“…Meng et al in [ 22 ] introduce an indoor localization which uses the magnetic sensor of the smartphone to localize a user. The magnetic map is formed with the help of local weight regression, as presented by Cleveland [ 23 ].…”

Section: Related Workmentioning

confidence: 99%

mPILOT-Magnetic Field Strength Based Pedestrian Indoor Localization

Ashraf

Hur

Park

2018

Sensors

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An indoor localization system based on off-the-shelf smartphone sensors is presented which employs the magnetometer to find user location. Further assisted by the accelerometer and gyroscope, the proposed system is able to locate the user without any prior knowledge of user initial position. The system exploits the fingerprint database approach for localization. Traditional fingerprinting technology stores data intensity values in database such as RSSI (Received Signal Strength Indicator) values in the case of WiFi fingerprinting and magnetic flux intensity values in the case of geomagnetic fingerprinting. The down side is the need to update the database periodically and device heterogeneity. We solve this problem by using the fingerprint database of patterns formed by magnetic flux intensity values. The pattern matching approach solves the problem of device heterogeneity and the algorithm’s performance with Samsung Galaxy S8 and LG G6 is comparable. A deep learning based artificial neural network is adopted to identify the user state of walking and stationary and its accuracy is 95%. The localization is totally infrastructure independent and does not require any other technology to constraint the search space. The experiments are performed to determine the accuracy in three buildings of Yeungnam University, Republic of Korea with different path lengths and path geometry. The results demonstrate that the error is 2–3 m for 50 percentile with various buildings. Even though many locations in the same building exhibit very similar magnetic attitude, the algorithm achieves an accuracy of 4 m for 75 percentile irrespective of the device used for localization.

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Section: Related Workmentioning

confidence: 99%

mPILOT-Magnetic Field Strength Based Pedestrian Indoor Localization

Ashraf

Hur

Park

2018

Sensors

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“…For this purpose, the data from several sources like Wi-Fi, Bluetooth, and pedestrian dead reckoning (PDR) is utilized either collectively or one can serve to provide a rough initial position which is refined with the data from the other sensors. For example, an indoor positioning approach is presented in [21] where the initial position calculated by Wi-Fi is used to restrict the search space in the magnetic field database. It helps to improve, both positioning accuracy and robustness.…”

Section: Related Workmentioning

confidence: 99%

A Comprehensive Analysis of Magnetic Field Based Indoor Positioning With Smartphones: Opportunities, Challenges and Practical Limitations

et al. 2020

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“…An additional finding is the impact of the area on localization accuracy, whereby the error can go up to 20 m within a larger area. Similarly, an indoor localization approach is introduced in [21] which builds the magnetic map using Local Weight Regression (LWR) presented by Cleveland [22]. The LWR leverages local data to fit points using polynomial weighted fitting where the polynomial coefficient is calculated through the least square method.…”

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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Robust Local Weighted Regression for Magnetic Map-Based Localization on Smartphone Platform

Cited by 6 publications

References 18 publications

mPILOT-Magnetic Field Strength Based Pedestrian Indoor Localization

mPILOT-Magnetic Field Strength Based Pedestrian Indoor Localization

A Comprehensive Analysis of Magnetic Field Based Indoor Positioning With Smartphones: Opportunities, Challenges and Practical Limitations

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

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