Research on the Magnetic Field Generator of Overhauser Magnetic Sensor

Magnetic sensor with direction memory can be used to control the motion direction. Based on Biot-Savart theory, the magnetic field distribution expression of a bar-type in external space is derived, and the superposition distribution of both inner and outer magnet is directed by the principle of superposition, which can be used to quantitatively describe the magnetic distribution formed by inner and outer magnet, and accurately scope the effective field. According to the operating characteristics of the magnetic reed switch with different value of pull-in and drop-out, by a proper detecting distance to ensure the magnetic field strength value of inner magnet at magnetic reed switch greater than pull-in value and less than drop-out value to make magnetic reed switch maintaining the original state when outer magnet leaving. Meanwhile, by a proper detecting distance to ensure the superinposed magnetic field strength value greater than pull-in value in the forward direction, and less than drop-out value in backward direction. Calculation of response curves show the impacts of magnet size, response intensity and detecting distance variation on the sensor.

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Magnetic Generator with Automatic Gain Regulation in a Wireless Tracking System for a Capsule Endoscope

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.401-403.1393 ◽

2013 ◽

Vol 401-403 ◽

pp. 1393-1396

Author(s):

Xu Dong Guo ◽

Chao Ruan ◽

Bin Ge ◽

Rong Guo Yan ◽

Ying Liu

Keyword(s):

Magnetic Field ◽

Signal To Noise Ratio ◽

Tracking System ◽

Reference Value ◽

Digital Signal ◽

Measuring Method ◽

Magnetic Sensor ◽

Capsule Endoscope ◽

The Magnetic Field ◽

Magnetic Generator

To track a capsule endoscope, a novel measuring method based on alternating magnetic field is presented. The signal-to-noise ratio of the magnetic sensor decreases sharply with the increasing tracking distance. Thus, a magnetic generator with automatic gain regulation is designed to improve the localization precision. It is composed of a microcontroller, a DA converter, a timer, a waveform synthesis circuit, a power amplifier, a sequence control circuit and excitation coils. First, the wireless magnetic sensor measures the strength of the magnetic field produced by the magnetic generator. Via radio frequency communication, the measured result is feedback to the comparator of the microcontroller. According to a deviation obtained by comparing the measured results with the reference value, the microcontroller outputs a digital signal to the DA converter to control the magnitude of the exciting current. The prototype of the system was developed and the experiment was performed. The experiment shows that the magnetic field generator can automatically adjust the strength of the exciting signal.

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Twelve-Position Calibrating Method without North Using for Three-Axis Magnetic Sensor

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.383-390.5082 ◽

2011 ◽

Vol 383-390 ◽

pp. 5082-5087

Author(s):

Ding Li ◽

Song Lin Wo ◽

Xiong Zhu Bu

Keyword(s):

Magnetic Field ◽

Scale Factor ◽

Magnetic Sensor ◽

Alignment Error ◽

Natural Conditions ◽

The Magnetic Field ◽

Iron Interference

In allusion to the compensation of environmental magnetic field for using three-axis magnetic sensor, a twelve-position calibrating method without north is designed. The equations of three-axis bias, scale factor error and install alignment error are deduced in the proposed method which is totally based on the magnetic field of up direction. According to the effect of hard iron and soft iron is equaled to the variation of three-axis bias, scale factor error and install alignment error, comparison experiments are completed in natural conditions and in conditions that the effect of hard iron and soft iron is artificially imposed. The experiment shows that this method can solve the compensation of hard iron and soft iron interference effectively for the application of up direction tumbling test.

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Indoor Positioning Using Magnetic Fingerprint Map Captured by Magnetic Sensor Array

Sensors ◽

10.3390/s21175707 ◽

2021 ◽

Vol 21 (17) ◽

pp. 5707

Author(s):

Ching-Han Chen ◽

Pi-Wei Chen ◽

Pi-Jhong Chen ◽

Tzung-Hsin Liu

Keyword(s):

Magnetic Field ◽

Sensor Array ◽

Probabilistic Neural Network ◽

Indoor Positioning ◽

Magnetic Sensor ◽

Magnetic Sensors ◽

Positioning System ◽

Positioning Accuracy ◽

Positioning Method ◽

The Magnetic Field

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.

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Sharp high-sensitivity high-temperature superconducting magnetic sensor successfully measures the magnetic field of the heart

Biosensors and Bioelectronics ◽

10.1016/0956-5663(93)80065-w ◽

1993 ◽

Vol 8 (7-8) ◽

pp. xv-xix

Keyword(s):

Magnetic Field ◽

High Temperature ◽

High Sensitivity ◽

Magnetic Sensor ◽

High Temperature Superconducting ◽

The Magnetic Field

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Ultra-lightweight integrated unmanned aerial systems for a wide range of geophysical magnetic exploration: a single system for high-precision archaeological surveys to rugged topography geological acquisitions

10.5194/egusphere-egu21-7290 ◽

2021 ◽

Author(s):

Jeanne Mercier de Lépinay ◽

Tristan Fréville ◽

Baptiste Kiemes ◽

Luis Miguel Sanabria ◽

Bruno Gavazzi ◽

...

Keyword(s):

Magnetic Field ◽

High Resolution ◽

High Precision ◽

Adaptive Systems ◽

Magnetic Measurements ◽

Magnetic Sensor ◽

Magnetic Data ◽

Industrial Sectors ◽

Wide Range ◽

The Magnetic Field

Magnetic mapping is commonly used in the academic and industrial sectors for a wide variety of objectives. To comply with a broad range of survey designs, the use of unmanned aerial vehicles (UAVs) has become frequent over the recent years. The majority of existing systems involves a magnetic acquisition equipment and its carrier (an UAV in this context) with no -or very few- connections between the two systems. Terremys is conceiving and optimizing UAVs specifically adapted for geophysical magnetic acquisitions together with the appropriate processing tools, and performs magnetic surveying in challenging environments. Terremys&#8217; &#8220;Q6&#8221; system weights 2.5 kg in air, including UAV & instrumentation, and allows 30 min swarm or individual flights.Rotary-wing UAVs are found to be the most adaptive systems for a wide range of contexts and constraints (extensive range of flights heights even with steep slopes). They offer more flight flexibility than fixed-wing aircrafts. One of the major problems in the use of rotary-wings UAVs for magnetic mapping is the magnetic field generated by the aircraft itself on the measurements. Towing the magnetic sensor 2 to 5 m under the aircraft reduces data positioning accuracy and decreases the performances of the UAV, which can be critical for high-resolution surveys. To overcome these problems, a deployable 1 m long boom&#160;is rigidly attached to the UAV. The UAV magnetic signal can be divided between 1-the magnetic field of the whole equipment and 2-a low to high frequency magnetic field mostly originating from the motors. The magnetization of the system is the principal source of magnetic noise. It is modelled and corrected by calibration-compensation processes permitted by the use of three-component fluxgate magnetometers. The time-varying noise depends on the motors rotational speed and is minimized by optimizing the UAV components and characteristics along with the boom&#8217;s length.The final set-up is able to acquire magnetic data with a precision of 1 to 5 nT at any height from 1 to 150 m above ground level. The high-precision magnetic measurements are coupled with a centimetric RTK navigation system to allow for high-resolution surveying. The quality of the obtained data is similar to that obtained with ground or aerial surveys with conventional carriers and matches industrial standards. Moreover, Terremys&#8217; systems merge in real-time data from all the aircraft instruments in order to integrate magnetic measurements, positioning information and all the UAV&#8217;s flight data (full telemetry) into a unique synchronized data file. This opens up many possibilities in terms of QA/QC, data processing and facilitates on-field workflows.Case studies with diverse designs, flight altitudes and targets are presented to investigate the acquisition performances for different applications, as distinct as network positioning, archaeological prospecting or geological mapping.The full integration of the magnetic sensor to the drone opens the possibility for implementation additional sensors to the system. The adjoining of other magnetic sensors would allow multi-sensors surveying and increases daily productivity. Diverse geophysical sensors can also be added, such as thermal/infrared cameras, spectrometers, radar/SAR.

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An Adaptive △M-ICCP Geomagnetic Matching Algorithm

Journal of Navigation ◽

10.1017/s0373463317000844 ◽

2017 ◽

Vol 71 (3) ◽

pp. 649-663 ◽

Cited By ~ 2

Author(s):

Jing Xiao ◽

Xiusheng Duan ◽

Xiaohui Qi

Keyword(s):

Magnetic Field ◽

Dynamic Environment ◽

Magnetic Sensor ◽

Matching Method ◽

Simulation Experiments ◽

Matching Algorithm ◽

Contour Point ◽

Novel Method ◽

The Magnetic Field ◽

Selection Of

In this paper, a novel method is proposed to generate the matching sequence of an ICCP algorithm for aircraft geomagnetic-aided navigation based on the M coding principle. The length of the matching sequence and the selection of the matching points directly affects the performance of the Iterated Closest Contour Point (ICCP) algorithm. This study proposes an adaptive geomagnetic matching method, ΔM-ICCP, to solve the problem of selecting suitable matching lengths, and matching points, when a vehicle is moving in a highly dynamic environment. First, the △M coding principle is adopted to select the matching points based on the information of the magnetic field, the resolution of the magnetic map, and the accuracy of the magnetic sensor. Then, the problem of selecting parameters for the △M-ICCP algorithm is turned into an optimisation problem, which can be solved by a Binary Particle Swarm Optimisation (BPSO) algorithm. Finally, the algorithm is verified through simulation experiments. The proposed algorithm can provide a basis to determine the matching length of the △M-ICCP algorithm and adaptively adjust the algorithm's parameters according to different trajectories. The algorithm is applicable even in the areas where the fluctuations of Earth's magnetic field are not significant.

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A Feedback Method to Improve the Dynamic Range and the Linearity of Magnetoimpedance Magnetic Sensor

Journal of Sensors ◽

10.1155/2019/2413408 ◽

2019 ◽

Vol 2019 ◽

pp. 1-8

Author(s):

Dongfeng He

Keyword(s):

Magnetic Field ◽

Steel Plate ◽

Dynamic Range ◽

Magnetic Sensor ◽

Magnetic Field Sensor ◽

Amorphous Wire ◽

Alloy Plate ◽

Field Sensor ◽

Feedback Method ◽

The Magnetic Field

We developed a high-sensitivity magnetoimpedance magnetic field sensor using a FeCoSiB amorphous wire and a coil wound around it. The amorphous wire had the diameter of 0.1 mm and the length of 5 mm. The magnetic field resolution of about 20 pT/√Hz was achieved. But the dynamic range of the magnetoimpedance magnetic field sensor was only about ±0.7 Gauss, which was not enough for some applications, such as the defect evaluation of steel plate. The linearity of the system was also not good when big magnetic field was applied, which will cause some noise when the system is used in unshielded environment. We developed a feedback method to improve the dynamic range and the linearity of the magnetic field sensor. The operation point of the magnetic field sensor was fixed by sending a feedback current to the coil. Using the feedback method, the dynamic range was improved from ±0.7 Gauss to ±10 Gauss and the linearity was also improved over 100 times better. An eddy current testing system using the magnetic sensor was developed, and the crack defects in steel plate and in 3D-printed titanium alloy plate were evaluated.

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AGV Magnetic Sensor Data Acquisition System

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.713-715.1056 ◽

2015 ◽

Vol 713-715 ◽

pp. 1056-1060 ◽

Cited By ~ 1

Author(s):

Wei Xin Wang

Keyword(s):

Magnetic Field ◽

Data Acquisition ◽

Data Acquisition System ◽

Magnetic Sensor ◽

Acquisition System ◽

Sensor Data ◽

Magnetic Data ◽

Soft Magnetic ◽

The Magnetic Field ◽

Sensor Data Acquisition

Based on PNI11096 magnetic sensor data acquisition system can be used for path recognition of automatic guided vehicle (AGV). The use of the magnetic field on the path to produce paving soft magnetic strip, AGV at run time through the sensor detects the current relative to the magnetic field strength of soft magnetic data will be returned in the STM32 chip data processing after two values through the open collector output 16 bit IO, provide location information for AGV.

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Evaluations of an ocean bottom electro-magnetometer and preliminary results offshore NE Taiwan

10.5194/gi-2019-13 ◽

2019 ◽

Author(s):

Ching-Ren Lin ◽

Chih-Wen Chiang ◽

Kuei-Yi Huang ◽

Yu-Hung Hsiao ◽

Po-Chi Chen ◽

...

Keyword(s):

Magnetic Field ◽

Power Consumption ◽

Field Experiments ◽

Magnetic Sensor ◽

Magnetic Data ◽

Ocean Bottom ◽

Data Logger ◽

Operating Current ◽

Ocean Bottom Seismographs ◽

The Magnetic Field

Abstract. The first stage of field experiments involving the design and construction of a low-power consumption ocean bottom electro-magnetometer (OBEM) has been completed. To improve the performance of the OBEM, we rigorously evaluated each of its units, e.g., the data loggers, acoustic parts, internal wirings, and magnetic and electric sensors, to eliminate unwanted events such as unrecovered or incomplete data. The evaluations of the procedure included the following. Data logger: digitizer sensitivity, linearity, and errors Acoustic transceiver: “ENABLE,” “DISABLE,” “RANGE,” “RELEASE1,” “RELEASE2,” and “OPTION1” functions Magnetic sensor: sensitivity of the fluxgate and orthogonality Electrical receiver: potential voltage, impedance, and frequency responses Power consumption: the maximum operating current of two sets of batteries Deployment and recovery procedures on deck We confirmed the optimal performance of the OBEM after repeatedly testing the procedures. The first offshore deployment of the OBEM together with ocean bottom seismographs (OBSs) was performed in NE Taiwan, where the water depth is approximately 1,400 m. The total intensity of the magnetic field (TMF) measured by the OBEM varied in the range of 44,100–44,150 nT, which corresponded to the proton magnetometer measurements. The daily variations of the magnetic field were recorded using the two horizontal components of the OBEM magnetic sensor. We found that the inclinations and magnetic data of the OBEM varied with two observed earthquakes when compared to the OBS data. The potential fields of the OBEM were slightly, but not obviously, affected by the earthquakes.

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