A Novel Indoor Positioning Technique Using Magnetic Fingerprint Difference

Kim, Binhee; Kong, Seung-Hyun

doi:10.1109/tim.2016.2566759

Cited by 41 publications

(25 citation statements)

References 17 publications

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“…Obviously, works have reported in literature which achieve fingerprinting accuracy in the sub-meter-range (e.g. UWB [4]- [6], MFI [7], [8]) however they require nonoff-the-shelf and usually expensive equipment.…”

Section: Discussionmentioning

confidence: 99%

“…Such techniques include Ultra-Wideband (UWB) [4]- [6] or Magnetic Field Intensity (MFI) [7], [8] technologies etc. Because of this, fingerprint-based positioning [9]- [11] which became a popular topic of research over the last decades, still remains an active area of research mainly because of its simplicity and its potential to be applicable using off-the-shelf technologies.…”

Section: Introductionmentioning

confidence: 99%

“…The accuracy seems to depend on the volume and quality of this context and it was proven that the more information included in the process the higher the probability for producing more accurate position estimates [20]- [22]. This information may include radio parameters such as Received Signal Strength Indication (RSSI) [23], Magnetic Field Intensity (MFI) [7] and Channel Impulse Responses (CIR) [24] which could also be cleverly combined or fused to improve the performance [25]. However, the sole dependence on radio parameters imposes various limits that are hard to overcome therefore attempts where diverted in the use of non-radio parameters such as inertial, temperature, light/illumination measurements etc.…”

Section: Introductionmentioning

confidence: 99%

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Multidevice Map-Constrained Fingerprint-Based Indoor Positioning Using 3-D Ray Tracing

Raspopoulos

2018

IEEE Trans. Instrum. Meas.

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Abstract-This paper studies the use of deterministic channel modelling through 3D Ray Tracing (RT) for constructing deviceindependent radiomaps for Wi-Fi RSSI-based fingerprinting indoor positioning, applicable to different devices. Device heterogeneity constitutes a limitation in fingerprint-based approaches and also constructing radiomaps through extensive in-situ measurement campaigns is laborious and time-consuming even with a single device let alone the need for radiomaps constructed using multiple different devices. This work tackles both challenges through the use of 3D RT for radiomap generation in conjunction with data calibration using a small set of device-specific measurements to make the radiomap device-independent. The efficiency of this approach is evaluated using simulations and measurements in terms of the time spent to generate the radiomap, the amount of device-specific data required for calibration and in terms of the achievable positioning accuracy. Potential accuracy improvements in the RT-based indoor positioning processes are further investigated, by studying the use of map constraints into the algorithm in the form of a-priori probabilities. In this approach, a Route Probability Factor (RPF), which reflects the likelihood of a user being in various locations inside the environment is used. The outcome of the evaluation process which includes a study of different RPF distributions, indicates the validity of the approach, demonstrated by a reduction in the positioning error for various devices. The versatility of this approach is also demonstrated for different scenarios, different devices and by considering different device-handling conditions.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multidevice Map-Constrained Fingerprint-Based Indoor Positioning Using 3-D Ray Tracing

Raspopoulos

2018

IEEE Trans. Instrum. Meas.

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“…A deep-learning approach utilizing CSI and RSSI to achieve high-precision outdoor positioning was proposed in [30]. Applications of CNN and the use of Wi-Fi and magnetic signals for indoor precise positioning were investigated in [31]. Software-defined radio devices and deep learning approaches were leveraged to improve indoor positioning accuracy [32].…”

Section: Related Workmentioning

confidence: 99%

A Novel Outdoor Positioning Technique Using LTE Network Fingerprints

Lei

Zhang

2020

Sensors

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In recent years, wireless-based fingerprint positioning has attracted increasing research attention owing to its position-related features and applications in the Internet of Things (IoT). In this paper, by leveraging long-term evolution (LTE) signals, a novel deep-learning-based fingerprint positioning approach is proposed to solve the problem of outdoor positioning. Considering the outstanding performance of deep learning in image classification, LTE signal measurements are converted into location grayscale images to form a fingerprint database. In order to deal with the instability of LTE signals, prevent the gradient dispersion problem, and increase the robustness of the proposed deep neural network (DNN), the following methods are adopted: First, cross-entropy is used as the loss function of the DNN. Second, the learning rate of the proposed DNN is dynamically adjusted. Third, this paper adopted several data enhancement techniques. To find the best positioning fingerprint and method, three types of fingerprint and five positioning models are compared. Finally, by using a deep residual network (Resnet) and transfer learning, a hierarchical structure training method is proposed. The proposed Resnet is used to train with the united fingerprint image database to obtain a positioning model called a coarse localizer. By using the prior knowledge of the pretrained Resnet, feed-forward neural network (FFNN)-based transfer learning is used to train with the united fingerprint database to obtain a better positioning model, called a fine localizer. The experimental results convincingly show that the proposed DNN can automatically learn the location features of LTE signals and achieve satisfactory positioning accuracy in outdoor environments.technologies require additional devices, making these systems impractical. Additionally, in signal NLOS propagation, the performance of the positioning systems will be greatly reduced [5].In contrast, wireless fingerprint positioning technology has received much attention owing to its simplicity and practicality with existing infrastructure and hardware. Many complex LTE signal cues are hidden in the surrounding environment, and the goal of wireless fingerprint-based localization is to discover these cues and use them effectively to determine UE positions [6]. Compared to satellite navigation positioning systems and other wireless positioning technologies, fingerprint-based positioning techniques have many merits. First, low-energy consumption sensors in UE require much less energy. Second, most of the fingerprint-based positioning technologies require no additional devices or infrastructure. Finally, fingerprint-based positioning can be achieved by using ubiquitous smartphones and signals from LTE base stations (BSs) [6,7]. Fingerprint positioning technology works in two phases: an offline training phase and an online matching phase [8]. During the training phase, signal measurements can be used, which are usually received signal strength indicator (RSSI), reference signal receivin...

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“…Magnetometer sensors use the magnetic field of the Earth and therefore have a stable allocentric reference direction. Localization systems combining inertial and magnetic sensors have been proposed for example in [14], [15].…”

Section: Circularity In Position Datamentioning

confidence: 99%

Automatic characterization of ambulatory patterns of utilitarian and leisure trips

Härmä¹,

Groot²

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-In the health self-management services it is beneficial to identify and address the already existing healthy activity patterns of the user. Some of these healthy activity patterns might be of a utilitarian nature, e.g. commuting to work by bike or on foot, or might be for leisure, like taking a walk in a park. In this paper we discuss one possibility to detect the utilitarian or leisure nature of a particular ambulatory path based on the geometry of the trajectory. In essence, a leisure trip is more commonly a round-trip while an utilitarian A-to-B trips follow the single shortest path between A and B. We define a generic measure for the characterization of utilitarian and leisure paths based on GPS location data and develop an algorithm for approaching the same based on only magnetometer data from a wearable device.

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A Novel Indoor Positioning Technique Using Magnetic Fingerprint Difference

Cited by 41 publications

References 17 publications

Multidevice Map-Constrained Fingerprint-Based Indoor Positioning Using 3-D Ray Tracing

Multidevice Map-Constrained Fingerprint-Based Indoor Positioning Using 3-D Ray Tracing

A Novel Outdoor Positioning Technique Using LTE Network Fingerprints

Automatic characterization of ambulatory patterns of utilitarian and leisure trips

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