A Survey of Magnetic-Field-Based Indoor Localization

Deep Neural Network-Based Fusion Localization Using Smartphones

Indoor location-based services (LBS) have tremendous practical and social value in intelligent life due to the pervasiveness of smartphones. The magnetic field-based localization method has been an interesting research hotspot because of its temporal stability, ubiquitousness, infrastructure-free nature, and good compatibility with smartphones. However, utilizing discrete magnetic signals may result in ambiguous localization features caused by random noise and similar magnetic signals in complex symmetric and large-scale indoor environments. To address this issue, we propose a deep neural network-based fusion indoor localization system that integrates magnetic and pedestrian dead reckoning (PDR). In this system, we first propose a ResNet-GRU-LSTM neural network model to achieve magnetic localization more accurately. Afterward, we put forward a multifeatured-driven step length estimation. A hierarchy GRU (H-GRU) neural network model is proposed, and a multidimensional dataset using acceleration and a gyroscope is constructed to extract more valid characteristics. Finally, more reliable and accurate pedestrian localization can be achieved under the particle filter framework. Experiments were conducted at two trial sites with two pedestrians and four smartphones. Results demonstrate that the proposed system achieves better accuracy and robustness than other traditional localization algorithms. Moreover, the proposed system exhibits good generality and practicality in real-time localization with low cost and low computational complexity.

Vortex generation due to multiple localized magnetic fields in the hybrid nanofluid flow - A numerical investigation

Vortices capture the attention of every scientist (as soon as they come into existence) while studying any flow problem because of their significance in comprehending fluid mixing and mass transport processes. A vortex is indeed a physical phenomenon that happens when a liquid or a gas flow in a circular motion. They are generated due to the velocity difference and may be seen in hurricanes, air moving across the plane wing, tornadoes, etc. The study of vortices is important for understanding various natural phenomena in different settings. This work explores the complex dynamics of the Lorentz force that drives the rotation of nanostructures and the emergence of intricate vortex patterns in a hybrid fluid with Fe₃O₄-Cu nanoparticles. The hybrid nanofluid is modeled as a single-phase fluid, and the partial differential equations (PDEs) that govern its behavior are solved numerically. This work also introduces a novel analysis that enables us to visualize the flow lines and isotherms around the magnetic strips in the flow domain. The Lorentz force confined to the strips causes the spinning of hybrid nanoparticles, resulting in complex vortex structures in the flow domain. The results indicate that the magnetic field lowers the Nusselt number by 34% while raising the skin friction by 9%. The Reynolds number amplifies the influence of the localized magnetic field on the flow dynamics. Lastly, the nano-scaled structures in the flow enhance the Nusselt number significantly while having a minor effect on the skin friction factor.

A Lightweight Approach to Localization for Blind and Visually Impaired Travelers

Independent wayfinding is a major challenge for blind and visually impaired (BVI) travelers. Although GPS-based localization approaches enable the use of navigation smartphone apps that provide accessible turn-by-turn directions in outdoor settings, such approaches are ineffective in indoor and other GPS-deprived settings. We build on our previous work on a localization algorithm based on computer vision and inertial sensing; the algorithm is lightweight in that it requires only a 2D floor plan of the environment, annotated with the locations of visual landmarks and points of interest, instead of a detailed 3D model (used in many computer vision localization algorithms), and requires no new physical infrastructure (such as Bluetooth beacons). The algorithm can serve as the foundation for a wayfinding app that runs on a smartphone; crucially, the approach is fully accessible because it does not require the user to aim the camera at specific visual targets, which would be problematic for BVI users who may not be able to see these targets. In this work, we improve upon the existing algorithm so as to incorporate recognition of multiple classes of visual landmarks to facilitate effective localization, and demonstrate empirically how localization performance improves as the number of these classes increases, showing the time to correct localization can be decreased by 51-59%. The source code for our algorithm and associated data used for our analyses have been made available in a free repository.

Indoor 2D Positioning Method for Mobile Robots Based on the Fusion of RSSI and Magnetometer Fingerprints

Received signal strength indicator (RSSI)-based fingerprinting is a widely used technique for indoor localization, but these methods suffer from high error rates due to various reflections, interferences, and noises. The use of disturbances in the magnetic field in indoor localization methods has gained increasing attention in recent years, since this technology provides stable measurements with low random fluctuations. In this paper, a novel fingerprinting-based indoor 2D positioning method, which utilizes the fusion of RSSI and magnetometer measurements, is proposed for mobile robots. The method applies multilayer perceptron (MLP) feedforward neural networks to determine the 2D position, based on both the magnetometer data and the RSSI values measured between the mobile unit and anchor nodes. The magnetic field strength is measured on the mobile node, and it provides information about the disturbance levels in the given position. The proposed method is validated using data collected in two realistic indoor scenarios with multiple static objects. The magnetic field measurements are examined in three different combinations, i.e., the measurements of the three sensor axes are tested together, the magnetic field magnitude is used alone, and the Z-axis-based measurements are used together with the magnitude in the X-Y plane. The obtained results show that significant improvement can be achieved by fusing the two data types in scenarios where the magnetic field has high variance. The achieved results show that the improvement can be above 35% compared to results obtained by utilizing only RSSI or magnetic sensor data.

Magnetic-Field-Based Indoor Positioning Using Temporal Convolutional Networks

Traditional magnetic-field positioning methods collect magnetic-field information from each spatial point to construct a magnetic-field fingerprint database. During the positioning phase, real-time magnetic-field measurements are matched to a magnetic-field map to predict the user's location. However, this approach requires a significant amount of time to traverse the entire magnetic-field fingerprint database and does not effectively leverage the magnetic-field sequence's unique patterns to improve the accuracy and robustness of the positioning system. In recent years, the application of deep learning for the indoor positioning of magnetic fields has grown rapidly, especially by using the magnetic-field sequence as a time series and a trained long short-term memory (LSTM) model to predict the position, directly avoiding the time-consuming matching process. However, the training of LSTM is time-consuming, and the degradation problem occurs as the stack of layers increases. This article proposes a temporal convolutional network (TCN)-based magnetic-field positioning system that extracts magnetic-field sequence features by preprocessing them with coordinate transformation, smoothing filtering, and first-order differencing. The proposed method is seamlessly applicable to heterogeneous smartphones. The trained TCN models are compared with the LSTM and gated recurrent unit (GRU) models, showing the high accuracy and robustness of the proposed algorithm.

Low Cost Magnetic Field Control for Disabled People

Our research presents a cost-effective navigation system for electric wheelchairs that utilizes the tongue as a human-machine interface (HMI) for disabled individuals. The user controls the movement of the wheelchair by wearing a small neodymium magnet on their tongue, which is held in place by a suction pad. The system uses low-cost electronics and sensors, including two electronic compasses, to detect the position of the magnet in the mouth. One compass estimates the magnet's position while the other is used as a reference to compensate for static magnetic fields. A microcontroller processes the data using a computational algorithm that takes the mathematical formulations of the magnetic fields as input in real time. The system has been tested using real data to control an electric wheelchair, and it has been shown that a trained user can effectively use tongue movements as an interface for the wheelchair or a computer.

Using Barometer for Floor Assignation within Statistical Indoor Localization

This paper presents methods for floor assignation within an indoor localization system. We integrate the barometer of the phone as an additional sensor to detect floor changes. In contrast to state-of-the-art methods, our statistical model uses a discrete state variable as floor information, instead of a continuous one. Due to the inconsistency of the barometric sensor data, our approach is based on relative pressure readings. All we need beforehand is the ceiling height including the ceiling's thickness. Further, we discuss several variations of our method depending on the deployment scenario. Since a barometer alone is not able to detect the position of a pedestrian, we additionally incorporate Wi-Fi, iBeacons, Step and Turn Detection statistically in our experiments. This enables a realistic evaluation of our methods for floor assignation. The experimental results show that the usage of a barometer within 3D indoor localization systems can be highly recommended. In nearly all test cases, our approach improves the positioning accuracy while also keeping the update rates low.

Analysis of Magnetic Field Measurements for Indoor Positioning

Infrastructure-free magnetic fields are ubiquitous and have attracted tremendous interest in magnetic field-based indoor positioning. However, magnetic field-based indoor positioning applications face challenges such as low discernibility, heterogeneous devices, and interference from ferromagnetic materials. This paper first analyzes the statistical characteristics of magnetic field (MF) measurements from heterogeneous smartphones. It demonstrates that, in the absence of disturbances, the MF measurements in indoor environments follow a Gaussian distribution with temporal stability and spatial discernibility. It shows the fluctuations in magnetic field intensity caused by the rotation of a smartphone around the Z-axis. Secondly, it suggests that the RLOWESS method can be used to eliminate magnetic field anomalies, using magnetometer calibration to ensure consistent MF measurements in heterogeneous smartphones. Thirdly, it tests the magnetic field positioning performance of homogeneous and heterogeneous devices using different machine learning methods. Finally, it summarizes the feasibility/limitations of using only MF measurement for indoor positioning.