The positioning and orientation system consists of three-axis generating coils and three-axis sensor coils in quasi-static magnetic field. The three-axis generating coils are fixed orthogonally and excited by the alternating current (AC) signals with different frequencies. They create the magnetic field that is equivalent to that generated by three orthogonal magnetic dipoles. Using the amplitude and phase information of the sensing signals in the sensor coils, the position and orientation parameters of the sensor coils can be computed by using an appropriate algorithm. In this paper, a novel algorithm is proposed to determine the position of the sensor coil object by some equations directly, so that its position and orientation parameters can be calculated much easier and faster. Based on this method, a system with support circuitry is designed with some special signal acquisition and sampling methods. Especially, a signal extraction (function fitting) method is proposed to pick up the coupling AC signal magnitude of the sensor coils, which simplifies the hardware circuitry and improves the signal acquisition accuracy. The simulation and real experimental results show that the system works satisfactorily with good accuracy.

A Novel Positioning and Orientation System Based on Three-Axis Magnetic Coils

Previous magnetic dipole localization algorithms using gradient data attempt to find the position of the magnetic source at the measurement time only. Based on the direct inversion of the magnetic gradient tensor, these methods provide results that can be highly sensitive to temporal noise in data. To avoid a temporally scattered solution, a recursive approach is proposed that is promising for estimating the trajectory and the magnetic moment components of a target modeled as a magnetic dipole source using data collected with a gradiometer. In this study, the determination of target position, magnetic moment, and velocity is formulated as a Bayesian estimation problem for dynamic systems, which could be solved using a sequential Monte Carlo based approach known as the “particle filter.” This filter represents the posterior distribution of the state variables by a system of particles which evolve and adapt recursively as new information becomes available. In addition to the conventional particle filter, the proposed tracking and classification algorithm uses the unscented Kalman filter (UKF) to generate the prior distribution of the unknown parameters. The proposed method is then demonstrated by applying it to real data collected when an automobile was passing by a gradiometer either on a straight or a curved track. The results indicate that the recursive method is less sensitive to noise than the direct inversion solution, even if not all the components of the gradient tensor were used.

Recursive Bayesian Method for Magnetic Dipole Tracking With a Tensor Gradiometer

Magnetic localization provides a feasible way to estimate the position and orientation of the wireless capsule endoscopy embedded with a permanent magnet. However, the magnetic localization suffers from the drifting effect from magnetic noises and the uncertain initial guess during the optimization. In this paper, a localization system with noise cancellation is proposed to reduce the drifting effect of the geomagnetic noise and obtain the initial guess in a simple and effective way. Compared to the prior arts, the proposed system is able to achieve a lower positioning and orientation error under the same system configuration and makes it possible for the patient to move around while wearing the localization system. Besides, the proposed algorithm improves the consistency of positioning error within the localization region. Within a volume of 380 mm by 270 mm by 240 mm covered by 16 digital magnetic sensors, the average positioning error obtained from the proposed algorithm is around 10 mm and the average orientation error is around 12°.

A Novel Passive Magnetic Localization Wearable System for Wireless Capsule Endoscopy

The position and orientation of an object embedding with a permanent magnet can be acquired in real time via a magnetic tracking system. However, one characteristic of the magnetic tracking technique is its varied tracking accuracy along with the tracking distance. Hence, this paper tends to investigate the relationship between the tracking accuracy and the distance from the magnet to the sensor array by both simulations and experiments. Results show that the relationship is expressed as a cubic polynomial equation, and that the equation coefficients are related to the properties of the magnet and the signal-to-noise ratio of the sensor outputs. When the magnet is located at a distance from 36 to 96 mm above the sensor array, the system had the best performance, while average localization and orientation errors were 0.70 mm and 1.22°, respectively. Thus, this system achieved the best tracking accuracy compared with the state-of-the-art of magnetic tracking systems. This paper is helpful to the researchers who want to implement a magnetic tracking system or need to know clearly the valid tracking distance of a magnetic tracking system.

Investigation of the Relationship Between Tracking Accuracy and Tracking Distance of a Novel Magnetic Tracking System

The Scalar Triangulation and Ranging (STAR) method, which is based upon the unique properties of magnetic gradient contraction, is a high real-time ferromagnetic target localization method. Only one measurement point is required in the STAR method and it is not sensitive to changes in sensing platform orientation. However, the localization accuracy of the method is limited by the asphericity errors and the inaccurate value of position leads to larger errors in the estimation of magnetic moment. To improve the localization accuracy, a modified STAR method is proposed. In the proposed method, the asphericity errors of the traditional STAR method are compensated with an iterative algorithm. The proposed method has a fast convergence rate which meets the requirement of high real-time localization. Simulations and field experiments have been done to evaluate the performance of the proposed method. The results indicate that target parameters estimated by the modified STAR method are more accurate than the traditional STAR method.

https://www.mdpi.com/1424-8220/16/12/2168/pdf

A Modified Magnetic Gradient Contraction Based Method for Ferromagnetic Target Localization

Detection and localisation of hidden magnetic objects is studied. The concomitant disturbance of the earth magnetic field is exploited. Magnetic fields and or gradients are measured with a number of sensors. Two point models for magnetic fields are selected for the analysis: the magnetic dipole and its quadrupole extension. It is shown how to apply least squares analysis of the data in order to detect and localise magnetised objects. The theoretical framework is tested in simulations, including noise. Two methods to do the minimisation are chosen. The formalism performs well in the simulations. Detection and localisation of the model objects is successful. No decisive differences in performance of the two minimisation methods are found. Results for the position parameters are reliable in any case. We have evaluated the goodness of fit in our simulations. Theoretical and simulated chi-square distributions are compared for different fit models and noise distributions with satisfactory results. The obtained parameter distributions are Gaussian. Measurements are performed to test the algorithms on real data collected for three ferromagnetic objects: a magnet, pipe and block. The magnet is easily localised, the pipe reasonably well. The block cannot be localised: its field variations cannot be distinguished from background field variations. Subsequent simulations show that using gradiometers improves the performances of the algorithms. The effects of a number of controllable parameters are evaluated. © The Institution of Engineering and Technology 2007.

Detection and localisation of magnetic objects

Popular Tasks used by Researchers

How can I help with your research?

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

We encountered an error while loading the panel. Please try refreshing the page.

Something went wrong

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

Anyone with the link can view this chat. You can switch it back to private anytime.

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Y. Barrell

Author Tools

Chat about Author

Papers

Detection and localisation of magnetic objects