Magnetic Positioning System Research Articles

Invariant Kalman Filter Design for Securing Robust Performance of Magnetic–Inertial Integrated Navigation System under Measurement Uncertainty

This study proposes an enhanced integration algorithm that combines the magnetic field-based positioning system (MPS—Magnetic Pose Estimation System) with an inertial system with the advantage of an invariant filter structure. Specifically, to mitigate the nonlinearity of the propagation model and perturbing effect from the estimated uncertainty, the formulation of the invariant Kalman filter was derived in detail. Then, experiments were conducted to validate the algorithm with an unmanned vehicle equipped with an IMU and MPS receiver. As a result, the navigation performance of the IEKF-based inertial and magnetic field integration system was presented and compared with the conventional Kalman filter results. Furthermore, the convergence and navigation performance were evaluated in the presence of state variable initialization errors. The findings indicate that the inertial and magnetic field coupled with the IEKF outperforms the typical KF approach, particularly when dealing with initial estimate uncertainties.

Design of Range/IMU-Aided Integrated Magnetic Positioning System for Improving Vertical Pose Estimation Performance

Design of Range/IMU-Aided Integrated Magnetic Positioning System for Improving Vertical Pose Estimation Performance

Model-aided 3D shape and force estimation of continuum robots based on Cosserat rod theory and using a magnetic localization system

Model-aided 3D shape and force estimation of continuum robots based on Cosserat rod theory and using a magnetic localization system

Design of AUVs based on 3D coils positioning and distributed base station control for nuclear spent fuel pools

An underwater magnetic induction positioning and communication system and an energy-efficient distributed control algorithm for underwater base stations are proposed in this paper. The positioning and communication system consists of a number of base stations and an Autonomous Underwater Vehicle (AUV) equipped with three-axis source coils respectively. The AUV receives this signal and based on its amplitude and phase information is able to locate the AUV and communicate with the base station. Due to the short positioning distance of magnetic induction positioning technology, a large number of base stations need to be installed underwater, which puts high demands on the control of the base stations. In this paper, an energy-efficient distributed control algorithm for underwater base stations is proposed to enable the AUV to meet the operational requirements while minimizing the total energy consumption of the base station. According to the simulation results, the design solves the problem that traditional underwater positioning and communication equipment cannot work stably for long periods of time in a high radiated environment, with a positioning error of no more than 10 cm within a preset operating range, and the algorithm proposed in this paper is able to reduce energy wastage by about 20%.

A 5-D Large-Workspace Magnetic Localization and Actuation System Based on an Eye-in-Hand Magnetic Sensor Array and Mobile Coils

Simultaneous magnetic localization and actuation in a human-scale workspace are challenging but essential for medical applications such as whole gastrointestinal (GI) tract monitoring and blood clot clearance in the leg. In this article, we propose a 5-D magnetic localization and actuation system that can track and actuate magnetic robots in a cylindrical workspace with a diameter of 500 mm. Based on the eye-in-hand configuration of the sensor array and the flexibility of the mobile electromagnetic manipulation system (mEMS), we realize a high localization accuracy in a large workspace with only 25 sensors. Moreover, we design an accurate magnetic field modeling method that can attenuate the electromagnets’ hysteresis and provide a precise actuation field estimation. Taking advantage of the mobile sensor array, we developed a signal-quality-based tracking strategy (STS) that could improve the localization performance by adjusting the movement of the sensor array. The effectiveness and advantages of the proposed methods are verified by simulations and experiments. Results show that the STS could decrease 25% position root-mean-square error (RMSE). The resulting position RMSE and orientation RMSE are 2.02 mm and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm {8.24}}^{\circ} $ </tex-math></inline-formula> , respectively. We experimentally prove the viability of the proposed strategy by propelling a magnetic helical robot through a 400-mm long 3-D twisted silicone phantom.

Comparison and discussion of intelligent optimization algorithms based on magnetic localization systems for improving the initial value defects of the L-M Algorithm

Magnetic localization techniques are used in in vivo therapy, drug delivery, and surgical localization widely due to their ability to locate in an unobstructed environment precisely. The Levenberg-Marquardt (L-M) algorithm is one of the most common magnetic localization algorithms due to its fast optimization speed and high precision, but it requires high initial values such that improper initial values can easily lead to local optimization. In this paper, the performance of ten commonly used intelligent optimization algorithms is compared based on the mathematical model of the permanent magnet. The final position of the magnet is obtained by a fusion algorithm that uses the result of the intelligent optimization algorithm as the initial value of L-M. The real-time localization accuracy of this fusion algorithm is verified on a hardware platform and experiments demonstrate that the integration of the TSA algorithm and L-M algorithm fusion yields higher performance of the magnetic positioning system.

A Three-Step Calibration Method of Sensors’ Pose for Magnetic Localization System

A Three-Step Calibration Method of Sensors’ Pose for Magnetic Localization System

Localization Using Global Magnetic Positioning System for Automated Driving Bus and Intervals for Magnetic Markers

This paper describes a localization method based on the global magnetic positioning system (GMPS) using a magneto-impedance sensor, which was developed for automated driving systems to obtain precise vehicle positions under all conditions, including snow or rain. The aim of this study is to verify the stability, robustness, and high accuracy of the proposed method, and propose appropriate intervals for magnetic markers. This localization method accurately calculates the sideslip angle because a noisy acceleration sensor is not required except for the identification process. The estimated positions and orientation are filtered through an extended Kalman filter as the vehicle passes the magnetic markers. The robust stability of the proposed method is verified using the small-gain theorem, which shows that the intervals for magnetic markers should be less than 38.6 m for a city bus. Experimental results using a city bus and GMPS show that there is a possibility that the intervals of magnetic markers are extended to 30 m at small curvature, and 10 m at large curvature from the current 2 m intervals. These results indicate that the maximum intervals to avoid lane deviation of vehicles depend on the reference path curvature.

Experimental Study About Cross Effect of Magnetic Positioning System with Wireless Power Transfer System

Experimental Study About Cross Effect of Magnetic Positioning System with Wireless Power Transfer System

Use of Magnetic Positioning Systems in Industrial and Biomedical Applications

Nowadays the ability to locate people and objects is becoming an important and necessary aspect for the wide range of applications concerning localization-based services. Indeed, applications based on spatial and time awareness of people and objects within certain contexts are being developed. Some application examples in which the positioning problem must be considered and solved are: industrial automation, virtual reality, smart cities, smart health, robot navigation and remote surgery. Although in outdoor environments this does not represent a big issue thanks to the presence and support of Global Navigation Satellite Systems (GNSSs), there is a whole series of scenarios, such as indoors, underground, and underwater environments, in which GNSSs fail due to the complexity of the considered environments with respect to radio propagation. Nowadays, in the considered scenarios, the identification of a reliable method able to satisfy the positioning requirements is an open issue, for which the measurement community can provide a valuable contribution regarding the proposal of measurement methods and procedures, the evaluation of the major causes of uncertainty of the devices and the consideration of these features within positioning algorithms and data processing.

Magnetic Matching-Aided Indoor Localization System Based on a Waist-Mounted Self-Contained Sensor Array

This paper presents a magnetic matching-aided indoor localization system based on a waist-mounted self-contained sensor array. Our purpose is to localize and track the elderly in nursing homes through the proposed wearable device to ensure their safety. The device consists of a low-cost 9-axis self-contained sensor array, a microcontroller, and a WiFi transmission module. This system uses the step length and heading-based pedestrian dead reckoning (PDR) framework as the backbone to estimate the user’s position using the averaged inertial data from the sensor array. A magnetic fingerprint matching (MFM) algorithm is introduced to constrain the drift of the PDR system. Meanwhile, we construct a single-step-based hybrid magnetic fingerprint model to improve the low discernibility of the magnetic field. Finally, we propose an augmented particle filter to fuse the PDR and the MFM algorithms to enhance the system performance further. Experimental results show that 95% of the positioning error after fusion is about 1.47 m, which is 99.3% higher than that of PDR, and the average positioning error after fusion is 0.55 m, which is 61.3% higher than that of PDR. Experimental results have successfully validated the effectiveness and high performance of the proposed magnetic matching-aided wearable indoor localization system.

First experience with Sirius Pintuition ® - A novel magnetic localization system for breast cancer surgery

Background: Sirius Pintuition® is a novel, wire-free magnetic seed technology for soft tissue localization. The seed can be detected up to 5 cm distance and the technology does not require adapted surgical instruments. This article presents a single-centre experience with the first 100 consecutive patients who underwent breast conserving surgery (BCS).

A Compensation Method for Relative Movement Between a Sensor Array and the Abdomen for Magnetic Localization of Capsule Endoscopes

Magnetic localization is an excellent candidate for localization of capsule endoscopes. A small magnet is embedded into the capsule, and its magnetic field is measured at a sensor array around the patient’s abdomen. However, relative movement between the abdomen and the array causes high localization errors. In this study, an innovative movement compensation method is proposed. A rigid array comprising 12 sensors was used to localize a permanent magnet. Furthermore, two orthogonal reference coils fed with a low-frequency signal were assumed to be fixed at the abdomen. The coils were alternately switched on and off, and thus, all three magnetic objects were separately localized since their magnetic fields can be distinguished in the time and frequency domains. A reference coordinate system was set up with the coils, in which the magnet was localized. The proposed compensation method was evaluated for relative rotations and displacements between the sensors and the three magnetic objects. Moreover, the compensation method was evaluated for two reference magnets and compared with the coil-based approach. The mean position error was reduced by approximately a factor of 10 from 41.5 ± 15.5 to 3.8 ± 1.1 mm, respectively, and the orientation error did not exceed 3° for the coil-based compensation method. In contrast, with two reference magnets, the mean position and orientation errors were higher than 30 mm and 20°, respectively. Consequently, it was shown that the coil-based method is the next step toward a wearable magnetic localization system to compensate for relative movement.

Gesture recognition of sign language alphabet with a convolutional neural network using a magnetic positioning system

&lt;p class="Abstract"&gt;&lt;span lang="EN-US"&gt;Gesture recognition is a fundamental step to enable efficient communication for the deaf through the automated translation of sign language. This work proposes the usage of a high-precision magnetic positioning system for 3D positioning and orientation tracking of the fingers and hands palm. The gesture is reconstructed by the MagIK (magnetic and inverse kinematics) method and then processed by a deep learning gesture classification model trained to recognize the gestures associated with the sign language alphabet. Results confirm the limits of vision-based systems and show that the proposed method based on hand skeleton reconstruction has good generalization properties. The proposed system, which combines sensor-based gesture acquisition and deep learning techniques for gesture recognition, provides a 100% classification accuracy, signer independent, after a few hours of training using transfer learning technique on well-known ResNet CNN architecture. The proposed classification model training method can be applied to other sensor-based gesture tracking systems and other applications, regardless of the specific data acquisition technology.&lt;/span&gt;&lt;/p&gt;

Magnetic Field-Based Vehicle Positioning System in Long Tunnel Environment

Recently, long tunnels are becoming more prevalent in Korea, and exits are added at certain sections of the tunnels. Thus, a navigation system should correctly guide the user toward the exit; however, adequate guidance is not delivered because the global navigation satellite system (GNSS) signal is not received inside a tunnel. Therefore, we present an accurate position estimation system using a magnetic field for vehicles passing through a tunnel. The position can be accurately estimated using the magnetic sensor of a smartphone with an appropriate attitude estimation and magnetic sensor calibration. Position estimation was realized by attaching the smartphone on the dashboard during navigation and calibrating the sensors using position information from the GNSS and magnetic field database before entering the tunnel. This study used magnetic field sequence data to estimate vehicle positions inside a tunnel. Furthermore, subsequence dynamic time warping was applied to compare the magnetic field data stored in the buffer with the magnetic field database, and the feasibility and performance of the proposed system was reviewed through an experiment in an actual tunnel. The analysis of the position estimation results confirmed that the proposed system could appropriately deliver tunnel navigation.

Demonstration of Feeding Vehicle‐Integrated Photovoltaic‐Converted Energy into the High‐Voltage On‐Board Network of Practical Light Commercial Vehicles for Range Extension

The setting up of a practical electrically driven light commercial demonstration vehicle with integrated photovoltaics (PV) is reported. The demonstrator vehicle is equipped with 15 modules based on the crystalline Si/amorphous Si heterojunction technology. The nominal total peak power under standard testing conditions is 2180 Wp. Specifically, the PV‐converted energy is fed into the high‐voltage (HV; 400 V) board‐net for a utilization of the large capacity of the HV battery and thus for direct range extension. The demonstrator vehicle is equipped with irradiation, wind, temperature, magnetic, and global positioning system sensors. Irradiation and temperature as well as the energy flows from modules, maximum power point trackers (MPPTs), low‐voltage buffer battery to HV battery via DC/DC, and from the HV battery to the loads during an exemplarily test drive day (May 31, 2021) are monitored. The range extension obtained at this day on our test route (51° 59′ N, 9° 31′ E) was 36 km, the corresponding CO2 savings account for ≈2.3 kg. The chain efficiency of the electronic components from the input side of the MPPTs to the HV output side of the DC/DC was 68.6%, whereas the DC/DC itself has an average efficiency of 90%.

Fuzzy rank cluster top k Euclidean distance and triangle based algorithm for magnetic field indoor positioning system

The indoor localisation based on indoor magnetic field (MF) has drawn much research attention since they have a range of applications field in science and industry. The position estimation is generally based on the Euclidean distance (ED) between compared data points. Commonly, the state-of-the-art k-nearest neighbour (KNN) algorithm is used to estimate the test point (TP) position by considering the average location of the closest estimated K reference points (RPs). However, the problem of using the KNN algorithm is the fixed K value does not guarantee accurate estimation at every position. In this study, we first optimise the MF RPs database using the clustering method. Each trained RP and other nearby RPs are clustered together at a certain distance. Then, we create a rank cluster algorithm where we match the top 10 ranks RPs with the nearest Euclidean distance to the TP with the RPs cluster. For the proposed fuzzy algorithm, a condition is applied to choose whether the triangle area or average Euclidean algorithm is used to find the final estimated position. Experiments show a localisation accuracy of 5.88 m, which is better than KNN with an improvement of 31 %.

Indoor Positioning Using Magnetic Fingerprint Map Captured by Magnetic Sensor Array.

By collecting the magnetic field information of each spatial point, we can build a magnetic field fingerprint map. When the user is positioning, the magnetic field measured by the sensor is matched with the magnetic field fingerprint map to identify the user’s location. However, since the magnetic field is easily affected by external magnetic fields and magnetic storms, which can lead to “local temporal-spatial variation”, it is difficult to construct a stable and accurate magnetic field fingerprint map for indoor positioning. This research proposes a new magnetic indoor positioning method, which combines a magnetic sensor array composed of three magnetic sensors and a recurrent probabilistic neural network (RPNN) to realize a high-precision indoor positioning system. The magnetic sensor array can detect subtle magnetic anomalies and spatial variations to improve the stability and accuracy of magnetic field fingerprint maps, and the RPNN model is built for recognizing magnetic field fingerprint. We implement an embedded magnetic sensor array positioning system, which is evaluated in an experimental environment. Our method can reduce the noise caused by the spatial-temporal variation of the magnetic field, thus greatly improving the indoor positioning accuracy, reaching an average positioning accuracy of 0.78 m.

Gesture Recognition of Sign Language Alphabet Using a Magnetic Positioning System

Hand gesture recognition is a crucial task for the automated translation of sign language, which enables communication for the deaf. This work proposes the usage of a magnetic positioning system for recognizing the static gestures associated with the sign language alphabet. In particular, a magnetic positioning system, which is comprised of several wearable transmitting nodes, measures the 3D position and orientation of the fingers within an operating volume of about 30 × 30 × 30 cm, where receiving nodes are placed at known positions. Measured position data are then processed by a machine learning classification algorithm. The proposed system and classification method are validated by experimental tests. Results show that the proposed approach has good generalization properties and provides a classification accuracy of approximately 97% on 24 alphabet letters. Thus, the feasibility of the proposed gesture recognition system for the task of automated translation of the sign language alphabet for fingerspelling is proven.

Induced Magnetic Field-Based Indoor Positioning System for Underwater Environments.

Autonomous underwater vehicles (AUV) are seen as an emerging technology for maritime exploration but are still restricted by the availability of short range, accurate positioning methods necessary, e.g., when docking remote assets. Typical techniques used for high-accuracy positioning in indoor use case scenarios, such as systems using ultra-wide band radio signals (UWB), cannot be applied for underwater positioning because of the quick absorption of the positioning medium caused by the water. Acoustic and optic solutions for underwater positioning also face known problems, such as the multi-path effects, high propagation delay (acoustics), and environmental dependency. This paper presents an oscillating magnetic field-based indoor and underwater positioning system. Unlike those radio wave-based positioning modalities, the magnetic approach generates a bubble-formed magnetic field that will not be deformed by the environmental variation because of the very similar permeability of water and air. The proposed system achieves an underwater positioning mean accuracy of 13.3 cm in 2D and 19.0 cm in 3D with the multi-lateration positioning method and concludes the potential of the magnetic field-based positioning technique for underwater applications. A similar accuracy was also achieved for various indoor environments that were used to test the influence of cluttered environment and of cross environment. The low cost and power consumption system is scalable for extensive coverage area and could plug-and-play without pre-calibration.