Geomagnetic Fingerprinting Research Articles

Geomagnetic indoor localisation-based dilated convolution neural networks

ABSTRACT With the rapid development of mobile Internet, the demand for Indoor Location-Based Service (ILBS) keeps rising. Geomagnetic field fingerprinting-based positioning scheme, with low cost, high accuracy and good stability, has gradually earned the attention of researchers. There are many problems in the existing indoor localisation researches based on geomagnetic fingerprint recognition, such as heavy workload of fingerprint collection, insufficient positioning accuracy, high walking cost of positioning and long positioning delay. Therefore, a localisation system named DCGIL is proposed in this thesis. In DCGIL, a segmentation length estimation algorithm of geomagnetic fingerprint sequence is proposed to evaluate the minimum segmentation length, the shortest walking distance. Then, through an overlapping fingerprint segmentation method, the gap between coordinate points and the cost of fingerprint matching are reduced. Further, dilated convolutional neural networks is utilised for segmented fingerprint classification, which could improve the accuracy of fingerprint classification compared with the existing classification methods. Finally, DCGIL utilises the sliding window to align and match the segmented fingerprint. Experiments show that the localisation accuracy reached 1.21 metres at 3.8 m shortest walking distance derived from segmentation length estimation algorithm, which improved 28% compared to existing algorithms.

Implicit Multimodal Crowdsourcing for Joint RF and Geomagnetic Fingerprinting

In fingerprint-based indoor localization, fusing radio frequency (RF) and geomagnetic signals has been shown to achieve promising results. To efficiently collect fingerprints, implicit crowdsourcing can be used, where signals sampled by pedestrians are automatically labeled with their locations on a map. Previous work on crowdsourced fingerprinting is often based on a single signal, which is susceptible to signal bias and labeling error. We study, for the first time, implicit multimodal crowdsourcing for joint RF and geomagnetic fingerprinting. The scheme, termed UbiFin, exploits the spatial correlation among RF, geomagnetic, and motion signals to mitigate the impact of sensor noise, leading to highly accurate and robust fingerprinting without the need for any explicit manual intervention. Using clustering and dynamic programming, UbiFin correlates spatially different signals and filters effectively mislabeled signals. We conduct extensive experiments on our campus and a large multi-story shopping mall. Efficient and simple to implement, UbiFin outperforms other state-of-the-art crowdsourcing schemes to construct RF and geomagnetic fingerprints in terms of accuracy and robustness (cutting fingerprint error by 40 percent in general).

An Algorithm with Iteration Uncertainty Eliminate Based on Geomagnetic Fingerprint under Mobile Edge Computing for Indoor Localization.

Indoor localization problems are difficult due to that the information, such as WLAN and GPS, cannot achieve enough precision for indoor issues. This paper presents a novel indoor localization algorithm, GeoLoc, with uncertainty eliminate based on fusion of acceleration, angular rate, and magnetic field sensor data. The algorithm can be deployed in edge devices to overcome the problems of insufficient computing resources and long delay caused by high complexity of location calculation. Firstly, the magnetic map is built and magnetic values are matched. Secondly, orientation updating and position selection are iteratively executed using the fusion data, which gradually reduce uncertainty of orientation. Then, we filter the trajectory from a path set. By gradually reducing uncertainty, GeoLoc can bring a high positioning precision and a smooth trajectory. In addition, this method has an advantage in that it does not rely on any infrastructure such as base stations and beacons. It solves the common problems regarding the non-uniqueness of the geomagnetic fingerprint and the deviation of the sensor measurement. The experimental results show that our algorithm achieves an accuracy of less than 2.5 m in indoor environment, and the positioning results are relatively stable. It meets the basic requirements of indoor location-based services (LBSs).

Research on Geomagnetic Indoor Positioning Based on Different Spatial Interpolation Methods

The resolution of geomagnetic field is low. In geomagnetic indoor positioning in large-scale scenes, with the increase of the number of fingerprints, the geomagnetic characteristic values corresponding to different position coordinates will have more similarities, resulting in a higher probability of fingerprint positioning mismatch. To solve this problem, Spline interpolation, RBF interpolation and Kriging interpolation are applied to the construction of geomagnetic fingerprint database in the offline stage, and BP neural network model is used for matching and positioning in the online stage. The experimental results show that the geomagnetic fingerprint database constructed by RBF interpolation can effectively improve the positioning accuracy and stability.

Pedometer-free Geomagnetic Fingerprinting with Casual Walking Speed

The geomagnetic field has been wildly advocated as an effective signal for fingerprint-based indoor localization due to its omnipresence and local distinctive features. Prior survey-based approaches to collect magnetic fingerprints often required surveyors to walk at constant speeds or rely on a meticulously calibrated pedometer (step counter) or manual training. This is inconvenient, error-prone, and not highly deployable in practice. To overcome that, we propose Maficon, a novel and efficient pedometer-free approach for geo ma gnetic fi ngerprint database con struction. In Maficon, a surveyor simply walks at casual (arbitrary) speed along the survey path to collect geomagnetic signals. By correlating the features of geomagnetic signals and accelerometer readings (user motions), Maficon adopts a self-learning approach and formulates a quadratic programming to accurately estimate the walking speed in each signal segment and label these segments with their physical locations. To the best of our knowledge, Maficon is the first piece of work on pedometer-free magnetic fingerprinting with casual walking speed. Extensive experiments show that Maficon significantly reduces walking speed estimation error (by more than 20%) and hence fingerprint error (by 35% in general) as compared with traditional and state-of-the-art schemes.

Improving indoor geomagnetic field fingerprinting using recurrence plot-based convolutional neural networks

ABSTRACT Geomagnetic field fingerprinting is gradually substituting Bluetooth and WiFi fingerprinting since the magnetic field is ubiquitous and independent of any infrastructure. Many studies have used Convolutional Neural Networks (CNNs) to develop indoor positioning systems. Most of these networks use actual magnetic values to build fingerprints. The main source of diminished accuracy is that these CNNs cannot solve the distribution issue of the same magnetic field values. To remedy this limitation, there is a recent interest in applying CNNs to sequences of actual and past data, but no comparative studies have shown the performance contribution of this alternative. In this paper, we propose a CNN-based magnetic fingerprinting system using Recurrence Plots (RPs) as sequence fingerprints. To fairly compare the proposed system with an existing solution treating instantaneous magnetic data, the same real-world data in an indoor environment are used. Testing results show location classification accuracies of 94.92% and 95.46% for the cases of using one RP and three RPs, respectively. As for the localisation error, results show that sequence pattern recognition results in at least a seven-fold decrease in mean distance error.

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

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).

Use of in-Hospital Geomagnetic Fingerprinting Localization.

Recently, visualizing location of people and things in a hospital has become an issue particularly for improving work efficiency and incident prevention. Although radio frequency waves such as Wi-Fi and Bluetooth are commonly used in indoor positioning, they have several limitations owing to their physical characteristics. We proposed in-hospital hybrid positioning technique, involving a combination of radio waves and geomagnetic fingerprinting techniques. We compared accuracy of proposed technique with that of Wi-Fi- and BLE-based techniques.

Efficient Indoor Localization Based on Geomagnetism

Geomagnetism is promising for indoor localization due to its omnipresence, high stability, and availability of magnetometers in smartphones. Previous works often fuse it with pedometer via particles, which are computationally intensive and make strong user behavior assumptions. To overcome that, we propose Magil, an approach leveraging geo mag netism for i ndoor l ocalization. To our best knowledge, this is the first piece of work using geomagnetism for smartphone localization without the need of pedometer or user walking model. Magil is applicable to any open or complex indoor environment. In the offline phase, Magil collects and stores geomagnetic fingerprints while surveyors walk indoors. In the online phase, it employs a fast algorithm to match the geomagnetic variation of the target with the stored fingerprints. Given closely matched segments, Magil constructs user trajectory efficiently with a modified shortest path formulation by selecting and connecting these matched segments. To further improve accuracy and deployability, we propose MagFi, which extends Mag il by fusing it with Wi- Fi . MagFi further collects opportunistic Wi-Fi RSSI for fingerprint construction. We have implemented both Magil and MagFi and conducted extensive experiments in our campus. Results show that our schemes outperform state-of-the-art schemes by a wide margin (often cutting localization error by 30%).

Ellipse Coefficient Map-Based Geomagnetic Fingerprint Considering Azimuth Angles

Geomagnetic fingerprint has been actively studied because of the high signal stability and positioning resolution even when the time has elapsed. However, since the measured three-axis geomagnetism signals at one position are irregular according to the change of the azimuth angle, a large-sized database which is stored magnitudes per angles is required for robust and accurate positioning against the change of the azimuth angle. To solve this problem, this paper proposes a novel approach, an elliptic coefficient map based geomagnetic fingerprint. Unlike the general fingerprint, which stores strength or magnitude of the geomagnetism signals depending on the position, the proposed algorithm minimized the size of databased by storing the Ellipse coefficient map through the ellipse equation derived from the characteristics of 2-D magnetic vectors depending on the position. In addition, the curvature bias of ellipse was reduced by applying the normalized linear least-squares method to 2-D geomagnetic characteristics and the positioning accuracy was improved by applying the weighted geomagnetic signal equalization method.

MPILOT-Magnetic Field Strength Based Pedestrian Indoor Localization.

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.

THE DESIGN AND IMPLEMENTATION OF INDOOR LOCALIZATION SYSTEM USING MAGNETIC FIELD BASED ON SMARTPHONE

Abstract. Sufficient signal nodes are mostly required to implement indoor localization in mainstream research. Magnetic field take advantage of high precision, stable and reliability, and the reception of magnetic field signals is reliable and uncomplicated, it could be realized by geomagnetic sensor on smartphone, without external device. After the study of indoor positioning technologies, choose the geomagnetic field data as fingerprints to design an indoor localization system based on smartphone. A localization algorithm that appropriate geomagnetic matching is designed, and present filtering algorithm and algorithm for coordinate conversion. With the implement of plot geomagnetic fingerprints, the indoor positioning of smartphone without depending on external devices can be achieved. Finally, an indoor positioning system which is based on Android platform is successfully designed, through the experiments, proved the capability and effectiveness of indoor localization algorithm.

GROPING: Geomagnetism and cROwdsensing Powered Indoor NaviGation

Although a large number of WiFi fingerprinting based indoor localization systems have been proposed, our field experience with Google Maps Indoor (GMI), the only system available for public testing, shows that it is far from mature for indoor navigation. In this paper, we first report our field studies with GMI, as well as experiment results aiming to explain our unsatisfactory GMI experience. Then motivated by the obtained insights, we propose GROPING as a self-contained indoor navigation system independent of any infrastructural support. GROPING relies on geomagnetic fingerprints that are far more stable than WiFi fingerprints, and it exploits crowdsensing to construct floor maps rather than expecting individual venues to supply digitized maps. Based on our experiments with 20 participants in various floors of a big shopping mall, GROPING is able to deliver a sufficient accuracy for localization and thus provides smooth navigation experience.