Magnetometer Sensor Research Articles

Adaptive Fault‐Tolerant Multiplicative Attitude Filtering for Small Satellites

ABSTRACTThis study tackles the problem of fault‐tolerant attitude estimation for small satellites. A probabilistic adaptive technique is presented for the multiplicative extended Kalman filter (MEKF) algorithm that is used in attitude estimation. The presented method is based on tracking the normalized measurement innovations in the filter and calculating the probability of the normal operation of the estimation system. Using this probability, the filter gain is corrected to maintain the tracking performance of the filter despite faulty measurements. In order to evaluate the performance of this method, several simulations are performed where different types of faults are introduced to the synthetic attitude sensor measurements (magnetometer and sun sensor) at different times. Simulation results are compared not only with a conventional EKF but also with another popular adaptive Kalman filter, an adaptive Kalman filter with multiple scaling factors (MSFs).

Copper permalloys for fluxgate magnetometer sensors

Abstract. Fluxgate magnetometers are commonly used to provide high-fidelity vector magnetic field measurements. The magnetic noise of the measurement is typically dominated by that intrinsic to a ferromagnetic core used to modulate (gate) the local field as part of the fluxgate sensing mechanism. A polycrystalline molybdenum–nickel–iron alloy (6.0–81.3 Mo permalloy) has been used in fluxgates since the 1970s for its low magnetic noise. Guided by previous investigations of high-permeability copper–nickel–iron alloys, we investigate alternative materials for fluxgate sensing by examining the magnetic properties and fluxgate performance of that permalloy regime in the range 28 %–45 % Cu by weight. Optimizing the alloy constituents within this regime enables us to create fluxgate cores with both lower noise and lower power consumption than equivalent cores based on the traditional molybdenum alloy. Racetrack geometry cores using six layers of ∼30 mm long foil washers consistently yield magnetic noise around 4–5 pT/Hz at 1 Hz and 6–7 pT/Hz at 0.1 Hz, meeting the 2012 1 s INTERMAGNET standard of less than 10 pT/Hz noise at 0.1 Hz.

Field‐Aligned Currents Associated With Pulsating Auroral Patches: Observation With Magneto‐Impedance Magnetometer (MIM) Onboard Loss Through Auroral Microburst Pulsations (LAMP) Sounding Rocket

AbstractWe made observations of magnetic field variations in association with pulsating auroras with the magneto‐impedance sensor magnetometer (MIM) carried by the Loss through Auroral Microburst Pulsations (LAMP) sounding rocket that was launched at 11:27:30 UT on 5 March 2022 from Poker Flat Research Range, Alaska. At an altitude of 200–250 km, MIM detected clear enhancements of the magnetic field by 15–25 nT in both the northward and westward components. From simultaneous observations with the ground all‐sky camera, we found that the footprint of LAMP at the 100 km altitude was located near the center of a pulsating auroral patch. The auroral patch had a dimension of ∼90 km in latitude and ∼25 km in longitude, and its major axis was inclined toward northwest. These observations were compared with results of a simple model calculation, in which local electron precipitation into the thin‐layer ionosphere causes an elliptical auroral patch. The conductivity within the patch is enhanced in the background electric field and as a result, the magnetic field variations are induced around the auroral patch. The model calculation results can explain the MIM observations if the electric field points toward southeast and one of the model parameters is adjusted. We conclude that the pulsating auroral patch in this event was associated with a one‐pair field‐aligned current that consists of downward (upward) currents at the poleward (equatorward) edge of the patch. This current structure is maintained even if the auroral patch is latitudinally elongated.

Advancing Objective Mobile Device Use Measurement in Children Ages 6–11 Through Built-In Device Sensors: A Proof-of-Concept Study

Mobile devices (e.g., tablets and smartphones) have been rapidly integrated into the lives of children and have impacted how children engage with digital media. The portability of these devices allows for sporadic, on-demand interaction, reducing the accuracy of self-report estimates of mobile device use. Passive sensing applications objectively monitor time spent on a given device but are unable to identify who is using the device, a significant limitation in child screen time research. Behavioral biometric authentication, using embedded mobile device sensors to continuously authenticate users, could be applied to address this limitation. This study examined the preliminary accuracy of machine learning models trained on iPad sensor data to identify the unique user of the device in a sample of children ages 6 to 11. Data was collected opportunistically from nine participants (8.2 ± 1.75 years, 5 female) in the sedentary portion of two semistructured physical activity protocols. SensorLog was downloaded onto study iPads and collected data from the accelerometer, gyroscope, and magnetometer sensors while the participant interacted with the iPad. Five machine learning models, logistic regression (LR), support vector machine, neural net (NN), k-nearest neighbors (k-NN), and random forest (RF), were trained using 57 features generated from the sensor output to perform multiclass classification. A train-test split of 80%–20% was used for model fitting. Model performance was evaluated using F1 score, accuracy, precision, and recall. Model performance was high, with F1 scores ranging from 0.75 to 0.94. RF and k-NN had the highest performance across metrics, with F1 scores of 0.94 for both models. This study highlights the potential of using existing mobile device sensors to continuously identify the user of a device in the context of screen time measurement. Future research should explore the performance of this technology in larger samples of children and in free-living environments.

An ensemble maximal feature subset selection for smartphone based human activity recognition

Smartphone-based Human Activity Recognition (HAR) uses spatiotemporal time series data collected from a smartphone’s in-built accelerometer, gyroscope, and magnetometer sensor. Real-time HAR datasets such as UCI-HAR and UCI-HAPT extract time and frequency domain statistical features for activity recognition from the collected time series signals. Various Machine Learning techniques have been proposed in the perspective of feature selection through ensemble, traditional, hybrid, and meta-heuristic-based techniques to improve the recognition performance. Though the feature selection techniques have shown promising results, the computational time and selection of valid significant features for better classification performance still need to be included. Moreover, these techniques are highly parameter-dependent and critical to threshold values used for experimentation. This research proposes a novel method of parameter-free ensemble feature selection by combining traditional feature selection algorithms with the concept of association rule mining to obtain the Maximal Feature Subset (MFS), which is used for training and testing the classification model for HAR. Mathematical association rule mining is framed rather than the traditional one to select the associated frequent feature(s) from the feature set evolved by traditional techniques. Experimentation has been carried out on UCI-HAR and UCI-HAPT, and the proposed method has increased the classification efficiency by selecting significant features and reduces computation time significantly.

Principle of Calibrating a Magnetometric Sensor by the Precise Measurement of Change in Its Spatial Position in a Constant Magnetic Field

Principle of Calibrating a Magnetometric Sensor by the Precise Measurement of Change in Its Spatial Position in a Constant Magnetic Field

Optimising logistics in intelligent transportation systems through CNN-IT: a convolutional neural network approach for enhanced efficiency and precision

This study addresses the critical challenge of optimising logistics within intelligent transportation systems (ITS) by introducing a novel approach leveraging convolutional neural network-IT (CNN-IT). The overarching objective is to enhance the efficiency and precision of logistics operations through advanced machine learning techniques. The methodology involves the design and implementation of a CNN-IT model trained on a diverse dataset comprising sensory data from various transportation modes, including cars, buses, and trains. The model’s architecture is carefully crafted to extract intricate patterns from the sensor data, enabling accurate classification of different vehicles. The process begins with the collection and preprocessing of a diverse dataset, encompassing accelerometer, gyroscope, magnetometer, and audio sensor data from various transportation modes. Results demonstrate the CNN-IT model’s remarkable accuracy, precision, recall, and F1 score, showcasing its capability to make well-informed predictions. The model achieved an outstanding accuracy of 92%, with precision, recall, and F1 score at 91%, 93%, and 92%, respectively. These metrics collectively reflect the model’s exceptional performance in accurately classifying transportation modes while maintaining a balanced trade-off between precision and recall. The strategic engineering of the model’s architecture to effectively handle sensor data and accurately categorise vehicles plays a significant role in optimising logistics operations.

Calibration of accelerometer and magnetometer sensors of MWD systems in directional drilling application

Measurement while drilling (MWD) tools include accelerometers, magnetometers and temperature sensors. The accuracy of these sensors are highly important in measuring inclination and Azimuth angle parameters in a directional well. These factors can increase costs when drilling a directional well. During the past years, many efforts have been made to increase the accuracy of these tools. Accuracy of accelerometer and magnetometer sensors are influenced by scale, bias and non-orthogonal errors. Considering that MWD systems work in a wide range of temperatures (for example 0 °C–150 °C), the temperature parameter should also be added to the factors affecting the errors. Here, after making a sample of MWD tool, a method to calibrate the tool using mathematical model based on total field calibration algorithm, least square method and the transfer matrix is presented. Calibrated parameters at ambient temperature are calculated with acceptable speed. Then, a method for calculating temperature coefficients in the desired range is provided. Compared to the case where temperature coefficients are not considered, the obtained results show a significantly increased accuracy of Inclination and Azimuth parameters through the use of these coefficients.

Estimating the Inertia Tensor Components of an Asymmetrical Spacecraft When Removing It from the Operational Orbit at the End of Its Active Life

The paper presents a method for estimating the inertia tensor components of a spacecraft that has expired its active life using measurement data of the Earth’s magnetic field induction vector components. The implementation of this estimation method is supposed to be carried out when cleaning up space debris in the form of a clapped-out spacecraft with the help of a space tug. It is assumed that a three-component magnetometer and a transmitting device are attached on space debris. The parameters for the rotational motion of space debris are estimated using this measuring system. Then, the known controlled action from the space tug is transferred to the space debris. Next, measurements for the rotational motion parameters are carried out once again. Based on the available measurement data and parameters of the controlled action, the space debris inertia tensor components are estimated. It is assumed that the measurements of the Earth’s magnetic field induction vector components are made in a coordinate system whose axes are parallel to the corresponding axes of the main body axis system. Such an estimation makes it possible to effectively solve the problem of cleaning up space debris by calculating the costs of the space tug working body and the parameters of the space debris removal orbit. Examples of numerical simulation using the measurement data of the Earth’s magnetic field induction vector components on the Aist-2D small spacecraft are given. Thus, the purpose of this work is to evaluate the components of the space debris inertia tensor through measurements of the Earth’s magnetic field taken using magnetometer sensors. The results of the work can be used in the development and implementation of missions to clean up space debris in the form of clapped-out spacecraft.

Locating undocumented orphaned oil and gas wells with smartphones

It is estimated that about 3 million orphaned and abandoned oil and gas wells exist in the U.S. that are leaking greenhouse gases and dangerous chemicals into the environment and thus pose both environmental and health risks to the public. Although about 130,000 of these wells have been identified, a large portion of them have either missing or incomplete documents making them difficult to locate and plug. Most of these wells have been out of use since early 1900s and may be missing wellheads or are rusted and covered by overgrown vegetation. Therefore, they are difficult to visibly notice and locate. However, the majority of them used one form of iron alloy casings. Such alloys are magnetic material that can be sensed by magnetometers. Most smartphones nowadays come with a magnetometer (as a compass) sensor. Here we utilize an iPhone 12 mini for the purpose of locating two abandoned wells (one with an exposed wellhead and one without any visible signs at the surface). We designed a simple unmanned aerial vehicle (UAV) survey setup where the iPhone 12 mini was put inside a custom-made sleeve and hung from a DJI Phantom 4 drone at about 1.5 m below the drone. We surveyed the two sites by flying the drone at three different altitudes, 10 m, 15 m, and 20 m above ground level (AGL). Our results show that at altitude of 10 m AGL the iPhone 12 mini magnetometer could pick the magnetic anomaly of either of the wells at intensities ≥52 μT; sufficient to accurately locate the wells. At altitude of 15 m AGL the iPhone 12 mini was able to locate the wells but within ∼5 m radius of the actual wells' location, and it was unable to detect any magnetic anomalies at 20 m AGL. This UAV setup cost about $2000 and was simple to operate. No data processing was done to create the magnetic maps showing where the wells were located in the survey areas. Thus, this setup is accessible to a broad range of users who are looking for ways to locate orphaned wells fast and easily.

Construction of features database using different platform navigation solutions for aiding PDR algorithm

Nowadays, navigation can be accomplished based on several technologies. Some of these technologies rely solely on camera vision-based algorithms, whereas others rely solely on motion sensors’ data. Furthermore, some solutions combine vision-based algorithms with data from motion sensors. In this study, three platform navigation algorithms (stereo visual odometry technique, combining the distance calculated from the visual optical flow with estimated heading from gyroscope sensor measurements, and combining the distance calculated from the visual optical flow with estimated heading from magnetometer sensor measurements) were compared to select the most accurate among them to be employed for features database construction. This features database, which contains various well-described features with known three-dimensional world coordinates, can be utilized to help any indoor pedestrian dead reckoning (PDR) navigation solution as well as mapping purposes. Tests' results demonstrated that vision-based technology (stereo visual odometry technique) is more accurate than the others however it should be aided with additional constraints to improve navigation positional accuracy. Afterward, this chosen technology was used to construct a well-described features database which was checked to assess its accuracy.

Enabling Boomless CubeSat Magnetic Field Measurements With the Quad‐Mag Magnetometer and an Improved Underdetermined Blind Source Separation Algorithm

AbstractIn situ magnetic field measurements are often difficult to obtain due to the presence of stray magnetic fields generated by spacecraft electrical subsystems. The conventional solution is to implement strict magnetic cleanliness requirements and place magnetometers on a deployable boom. However, this method is not always feasible on low‐cost platforms due to factors such as increased design complexity, increased cost, and volume limitations. To overcome these problems, we propose using the Quad‐Mag CubeSat magnetometer with an improved Underdetermined Blind Source Separation (UBSS) noise removal algorithm. The Quad‐Mag consists of four magnetometer sensors in a single CubeSat form‐factor card that allows distributed measurements of stray magnetic fields. The UBSS algorithm can remove stray magnetic fields without prior knowledge of the magnitude, orientation, or number of noise sources. UBSS is a two‐stage algorithm that identifies signals through cluster analysis and separates them through compressive sensing. We use UBSS with single‐source point detection to improve the identification of noise signals and iteratively‐weighted compressed sensing to separate noise signals from the ambient magnetic field. Using a mock CubeSat, we demonstrate in the lab that UBSS reduces four noise signals producing more than 100 nT of noise at each magnetometer to below the expected instrument resolution (5 nT at 65 Hz). Additionally, we show that the integrated Quad‐Mag and improved UBSS system works well for 1U, 2U, 3U, and 6U CubeSats in simulation. Our results show that the Quad‐Mag and UBSS noise cancellation package enables high‐fidelity magnetic field measurements from a CubeSat without a boom.

Reliability and measurement properties of upper cervical flexion-extension range of motion testing in people with cervicogenic headache and asymptomatic controls

ABSTRACT Objective The objective was to determine reliability of sagittal plane range of motion (ROM) assessment in a healthy population and in people with cervicogenic headache (CGH). Methods Upper cervical flexion/extension ROM was measured using an iPhone magnetometer sensor and retraction/protraction ROM measured by linear displacement. Two independent raters evaluated these movements in 33 subjects with CGH and 33 age and gender matched asymptomatic healthy controls on two occasions. Measurement procedures were standardized; and the order of testing randomized. Reliability, standard error of measurement (SEM) and minimum detectable change (MDC) were calculated. Results Subjects comprised 30 females and 36 males. The iPhone method demonstrated high reliability (ICC >0.82) with SEM values ranging from 0.62 to 1.0 and MDC values ranging from 1.70 to 4.81 respectively. Evaluation of linear displacement demonstrated moderate reliability (ICC >64). with SEM values ranging from 0.38 to 1.91 and MDC values ranging from 1.05 to 1.63 respectively. Compared to healthy controls, upper cervical flexion, retraction and protraction ROM was significantly less in the CGH group with mean difference of 6.50°, 1.52 cm and 2.34 cm respectively. Discussion Upper cervical spine sagittal plane ROM can be measured with moderate to high reliability and was found to be more restricted in people with CGH.

Unscented Kalman Filter Based Attitude Estimation Using Magnetometer and Sun Sensor Aided Mems Gyros

The estimation of attitude of small spacecraft, limited by space and power, is achieved by using rate class MEMS gyros. Considerable drift in these sensors limits the use of inertial algorithms. Secondary sensors need to be used for the real time estimation of the drift. This paper proposes an algorithm based on Unscented Kalman Filter (UKF) which utilizes three axis magnetometer and sun sensor as secondary sensors and performs data fusion on to the gyro measurements. A 7-state UKF is used for the purpose. The paper discusses the detailed derivation of the algorithm, and the sensor models. Reference models for magnetometer and sun sensor is also included. Ability of the algorithm to cater to the effects of solar eclipse and sensor data loss for short duration are also studied.

Attitude filtering with uncertain process and measurement noise covariance using SVD‐aided adaptive UKF

AbstractIt is presented in this article how to simultaneously alter the process and measurement noise covariance matrices for nontraditional attitude filtering technique. The unscented Kalman filter (UKF) and singular value decomposition (SVD) methods are integrated in the nontraditional attitude filtering algorithm to estimate a nanosatellite's attitude with an inherent robustness feature. The SVD approach determines the attitude of the nanosatellite and provides one estimate at a single frame utilizing measurements from the magnetometer and Sun sensor as the initial stage of the algorithm. These attitude terms are subsequently fed into the UKF with their error covariances, which makes the filter robust inherently. The attitude estimations of the satellite are compared between the filters presented. The Q (process noise covariance) adaption approach with multiple scale factors is specifically suggested for differences in between the process channels. Performance of the multiple scale factors‐based adaptive SVD‐aided UKF is examined in the event of process noise increase, which may result from changes in the environment or satellite dynamics.

SmartMagnet: Proximity-Based Access Control for IoT Devices With Smartphones and Magnets

Ubiquitous smartphones can be powerful tools to access IoT devices. Proximity-based access control (PBAC) is needed such that IoT devices only allow data access by legitimate users in close proximity. Traditional smartphone-based authentication techniques do not satisfy the PBAC requirements. This paper presents SmartMagnet, a novel scheme that combines smartphones and cheap magnets to achieve PBAC for IoT devices. SmartMagnet explores a few cheap, tiny commodity magnets which we propose to attach to or embed into IoT devices, as well as the magnetometer and attitude sensor on commodity smartphones. Each legitimate user performs a self-chosen 3D password gesture near the target IoT device with the enrolled smartphone. Then the system server uses the IoT device's confidential magnet configuration parameters to reconstruct the user gesture from the magnetometer and attitude sensor data submitted by the smartphone. If the reconstructed gesture matches the stored template of the purported user, the smartphone user is deemed legitimate and allowed access to the IoT device. Extensive experiments confirm the high usability of SmartMagnet and its strong resilience to lost/stolen smartphones and also remote attacks via signal relaying.

Quadcopter Navigation System With Waypoint Method Through Flight Controller at Ground Station

In a quadcopter, a navigation system is needed to monitor the position and control the movement of the quadcopter. In recent years, a navigation system based on longitude and latitude coordinates has been developed, namely a waypoint system. This flight control system works to maintain the actual position and direction of the quadcopter to its destination. Thus, the authors designed a navigation control system using the waypoint method on a quadcopter which can walk autonomously from one coordinate point to another coordinate point or destination. This navigation system works using GPS (Global Positioning System), and GY-81 as a magnetometer sensor. The application of the waypoint method can help the robot to determine the direction toward the destination and how far the target is from the robot's current position. This is evidenced in the movement of the robot with an accuracy value of up to 99.48% and an average time to reach the target in about 50-65 seconds. The application of the PID method gives better heading results compared to those without using the PID method. This is evidenced by the stable robot movement and fairly accurate compass sensor readings. Based on the test results using the PID method on the seacoast, the error = 2.15% and the accuracy = 97.85%, while in the test without the PID method on the land surface, the error = 3.33% and the accuracy = 96.6%. For testing using the PID method in paddy fields, an error result = 1.85% and an accuracy result = of 98.1%, while testing without the PID method on the surface of the water obtained an error result = 2.75% and an accuracy result = 97.25%.

Design Of Multi Sensor Quadcopter Navigation Data Acquisition For Early Fire Detection and Treatment

Recently, forest and land fires have become an event that has become a concern for the national and international community. This research creates a quadcopter as a tool for detecting forest and land fires. This system is an alternative tool for detecting forest and land fires that already exist. The advantage of this quadcopter is that it can collect data before the fire, during the fire, and after the fire. This quadcopter is designed and realized the developm­­­­­ent of an automatic navigation system for unmanned aircraft. This system is controlled by a microcontroller that processes IMU (Inertial Measurement Unit) sensor data in which there is a gyroscope and accelerometer, GPS, and magnetometer so that it can fly automatically. The purpose of this study is to acquire data on the navigation sensors used in the unmanned aerial vehicle (UAV) design, then the data from the navigation sensors will be combined using complement and median filters as position control input for the quadcopter. Based on this research, processing from sensor readings to sensor integration is able to produce better data, where the quadcopter's angle of view has an error percentage of 1.10° and 0.68°. This value is smaller than the percentage error of the magnetometer sensor alone or the IMU sensor alone which results in an error percentage of 1.18° and 0.76°.

Extended Kalman Filter Design to Estimate the Attitude of the Nanosatellite System Using Magnetometer

The problem of estimating nonlinear systems is an important issue in many engineering applications. One of these applications is in the nanosatellite for Vehicle-to-everything (V2X) communications, which is used to replace sensors in the event of failure or error. Therefore, the accuracy of pre-filter estimation is of great importance. To control the orientation of a satellite, it is important to estimate the attitude accurately. Time-series estimation is especially important in micro and nanosatellites, whose sensors are usually low-cost and have higher noise levels than high-end sensors. Also, the algorithms should be able to run on systems with very restricted computer power. In this paper, an overview of the algorithms used to determine the attitude of nanosatellites, and especially using only the magnetometer sensor, will be discussed. This paper aims to simulate a nanosatellite system to estimate the magnetic field derivative vector using only the magnetometer data with the desired accuracy. The estimated vectors in the pre-filter, along with the magnetometer sensor data, are used to estimate the attitude of the satellite. The estimation of the state vector consisting of the vector part of quaternion and the angular velocity vector is used for the control calculation. To evaluate the accuracy of this pre-filter, a comparison of the magnetometer vector estimator with the body sensor data has been used.

Design and Development of a Family of Integrated Devices to Monitor Animal Movement in the Wild

Monitoring the tortoise Chelonoidis chilensis in the wild, currently in a vulnerable state of conservation in southern Argentina, is essential to gather movement information to elaborate guidelines for the species preservation. We present here the electronic circuit design as well as the associated firmware for animal monitoring that was entirely designed by our interdisciplinary research team to allow the extension of device features in the future. Our development stands out for being a family of low-cost and low-power devices, that could be easily adaptable to other species and contexts. Each device is composed of a sub 1 GHz radiofrequency IoT-compatible transceiver, a global navigation satellite system (GNSS) receiver, a magnetometer, and temperature and inertial sensors. The device does not exceed 5% of the animal's weight to avoid disturbance in their behavior. The board was designed to work as a monitoring device as well as a collecting data station and a tracker, by adding only small pieces of hardware. We performed field measurements to assess the autonomy and range of the radiofrequency link, as well as the power consumption and the associated positioning error. We report those values and discuss the device's limitations and advantages. The weight of the PCB including battery and GNSS receiver is 44.9 g, its dimensions are 48.7 mm × 63.7 mm, and it has an autonomy that can vary between a week and a month, depending on the sampling rates of the sensors and the rate of the RF signal and that of the GNSS receiver. The characterization of the device parameters will favor the open use of this development by other research groups working on similar projects.