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This article investigates the causes of occasional flight instability observed in Unmanned Aerial Vehicles (UAVs). The issue manifests as unexpected oscillations that can lead to emergency landings. The analysis focuses on delays in the Extended Kalman Filter (EKF) algorithm used to estimate the drone's attitude, position, and velocity. These delays disrupt the flight stabilization process. The research identifies two potential causes for the delays. First cause is magnetic field distrurbances created by UAV motors and external magnetic fields (e.g., power lines) that can interfere with magnetometer readings, leading to extended EKF calculations. Second cause is EKF fusion step implementation of the PX4-ECL library combining magnetometer data with other sensor measurements, which can become computionally expensive, especially when dealing with inconsistent magnetic field readings. This can significantly increase EKF processing time. The authors propose a solution of moving the magnetic field estimation calculations to a separate, lower-priority thread. This would prevent them from blocking the main EKF loop and causing delays. The implemented monitoring techniques allow for continuous observation of the real-time operating system's behavior. Since addressing the identified issues, no significant problems have been encountered during flights. However, ongoing monitoring is crucial due to the infrequent and unpredictable nature of the disturbances. [ABSTRACT FROM AUTHOR]

Normally, a three-dimensional orientation determination algorithm that is used in a magnetic and inertial measurement unit calculates the inclination (including both the pitch and roll) of rigid bodies by fusing the measurements of the gyroscope, as well as the measurements of both the accelerometer and the magnetometer. The measurements of the magnetometer can be helpful in improving the inclination estimation accuracy; however, once the measurements of the magnetometer are disturbed by ferromagnetic materials, the inclination estimation accuracy could be significantly decreased. Hence, a better approach should be followed in terms of not employing the measurements of the magnetometer for inclination determination. In order to achieve this goal, the component of the measurement of the magnetometer that is used for the improvement of the inclination estimation accuracy, along with the measurement of the accelerometer at each sampling time instant, is abandoned. Consequently, the remaining component of the measurement of the magnetometer, which is perpendicular to the measurement of the accelerometer, is used for the azimuth determination. After applying this process, the extended Kalman filter (EKF) is proposed for the inclination and azimuth estimations. Through experiments, the EKF is compared with three algorithms that were recently proposed with the same objective as this work, and the extracted outcomes show that the EKF approach clearly outperforms these three algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes a new methodology for the automatic detection of magnetic disturbances from magnetic inertial measurement unit (MIMU) sensors based on deep learning. The proposed approach considers magnetometer data as input to a long short-term memory (LSTM) neural network and obtains a labeled time series output with the posterior probabilities of magnetic disturbance. We trained our algorithm on a data set that reproduces a wide range of magnetic perturbations and MIMU motions in a repeatable and reproducible way. The model was trained and tested using 15 folds, which considered independence in sensor, disturbance direction, and signal type. On average, the network can adequately detect the disturbances in 98 % of the cases, which represents a significant improvement over current threshold-based detection algorithms. [ABSTRACT FROM AUTHOR]

Robust and accurate attitude and heading estimation using Micro-Electromechanical System (MEMS) Inertial Measurement Units (IMU) is the most crucial technique that determines the accuracy of various downstream applications, especially pedestrian dead reckoning (PDR), human motion tracking, and Micro Aerial Vehicles (MAVs). However, the accuracy of the Attitude and Heading Reference System (AHRS) is often compromised by the noisy nature of low-cost MEMS-IMUs, dynamic motion-induced large external acceleration, and ubiquitous magnetic disturbance. To address these challenges, we propose a novel data-driven IMU calibration model that employs Temporal Convolutional Networks (TCNs) to model random errors and disturbance terms, providing denoised sensor data. For sensor fusion, we use an open-loop and decoupled version of the Extended Complementary Filter (ECF) to provide accurate and robust attitude estimation. Our proposed method is systematically evaluated using three public datasets, TUM VI, EuRoC MAV, and OxIOD, with different IMU devices, hardware platforms, motion modes, and environmental conditions; and it outperforms the advanced baseline data-driven methods and complementary filter on two metrics, namely absolute attitude error and absolute yaw error, by more than 23.4% and 23.9%. The generalization experiment results demonstrate the robustness of our model on different devices and using patterns. [ABSTRACT FROM AUTHOR]

Compared with the traditional needle driving method, hybrid maglev needle driving is a new weft knitting machine technology, which alleviates the problems of noise, heat, and needle breakage. However, in the structure of needle arrays, magnetic disturbance between permanent magnet knitting needles leads to unstable needle control. Therefore, this paper attempts to solve this problem through a performance analysis of hybrid maglev needle driving. Based on the structure, the magnetic force distribution model of permanent magnet knitting needles is established. Aiming at the magnetic interference between magnetic arrays, a magnetic shielding material, silicon steel with a high permeability, is proposed to optimize the driving structure of a magnetic levitation needle array. Through simulation and experimental analysis, the influence of different silicon steel thicknesses on magnetic field shielding is analyzed. It is concluded that the optimal value of a silicon steel sheet is 1 mm and that the optimal gauge of hybrid maglev knitting needles is 8 mm. Finally, compared with the theoretical and simulation analysis, the experimental results have indicated that the proposed optimized structure of the gauge of hybrid maglev knitting needles is correct and effective. [ABSTRACT FROM AUTHOR]

This paper proposes a new methodology for the automatic detection of magnetic disturbances from magnetic inertial measurement unit (MIMU) sensors based on deep learning. The proposed approach considers magnetometer data as input to a long short-term memory (LSTM) neural network and obtains a labeled time series output with the posterior probabilities of magnetic disturbance. We trained our algorithm on a data set that reproduces a wide range of magnetic perturbations and MIMU motions in a repeatable and reproducible way. The model was trained and tested using 15 folds, which considered independence in sensor, disturbance direction, and signal type. On average, the network can adequately detect the disturbances in 98% of the cases, which represents a significant improvement over current threshold-based detection algorithms.

Normally, a three-dimensional orientation determination algorithm that is used in a magnetic and inertial measurement unit calculates the inclination (including both the pitch and roll) of rigid bodies by fusing the measurements of the gyroscope, as well as the measurements of both the accelerometer and the magnetometer. The measurements of the magnetometer can be helpful in improving the inclination estimation accuracy; however, once the measurements of the magnetometer are disturbed by ferromagnetic materials, the inclination estimation accuracy could be significantly decreased. Hence, a better approach should be followed in terms of not employing the measurements of the magnetometer for inclination determination. In order to achieve this goal, the component of the measurement of the magnetometer that is used for the improvement of the inclination estimation accuracy, along with the measurement of the accelerometer at each sampling time instant, is abandoned. Consequently, the remaining component of the measurement of the magnetometer, which is perpendicular to the measurement of the accelerometer, is used for the azimuth determination. After applying this process, the extended Kalman filter (EKF) is proposed for the inclination and azimuth estimations. Through experiments, the EKF is compared with three algorithms that were recently proposed with the same objective as this work, and the extracted outcomes show that the EKF approach clearly outperforms these three algorithms.

Robust and accurate attitude and heading estimation using Micro-Electromechanical System (MEMS) Inertial Measurement Units (IMU) is the most crucial technique that determines the accuracy of various downstream applications, especially pedestrian dead reckoning (PDR), human motion tracking, and Micro Aerial Vehicles (MAVs). However, the accuracy of the Attitude and Heading Reference System (AHRS) is often compromised by the noisy nature of low-cost MEMS-IMUs, dynamic motion-induced large external acceleration, and ubiquitous magnetic disturbance. To address these challenges, we propose a novel data-driven IMU calibration model that employs Temporal Convolutional Networks (TCNs) to model random errors and disturbance terms, providing denoised sensor data. For sensor fusion, we use an open-loop and decoupled version of the Extended Complementary Filter (ECF) to provide accurate and robust attitude estimation. Our proposed method is systematically evaluated using three public datasets, TUM VI, EuRoC MAV, and OxIOD, with different IMU devices, hardware platforms, motion modes, and environmental conditions; and it outperforms the advanced baseline data-driven methods and complementary filter on two metrics, namely absolute attitude error and absolute yaw error, by more than 23.4% and 23.9%. The generalization experiment results demonstrate the robustness of our model on different devices and using patterns.

Abstract The stray current of direct current (DC) railway systems causes magnetic disturbance in geomagnetic measurements, which may complicate the identification of useful information. The magnetic disturbance exhibits broadband characteristics in the frequency domain. In this paper, we propose a noise reduction method based on the adaptive Kalman filter to extract useful signals from the geomagnetic data with a high level of noise. The covariance matrixes of both the process noise (Q) and measurement noise (R) can be adaptively estimated to improve the performance of the adaptive Kalman filter. The proposed method is adopted to process the geomagnetic data collected at the Beijing Geomagnetic Observatory (BJI), which is affected by the DC railway system. The magnetic disturbance is largely reduced, and the signal-to-noise ratios of the horizontal and vertical components of the geomagnetic field are improved by more than 14 dB and 27 dB, respectively. The K-indices are calculated to evaluate the performance of the adaptive Kalman filter method. To assess the influence of the adaptive Kalman filter on natural signals, the geomagnetic data that contain rapid variations are processed. The denoising results show that the adaptive Kalman filter can effectively reduce the magnetic disturbance caused by DC railway system without large impact on the natural geomagnetic rapid variations.

• Analysis of magnetic cleanliness of instruments on space missions using a Python program. • Analysis conducted on the soon-to-be-launched Korea Pathfinder Lunar Orbiter (KPLO). • Magnetic interference from the spacecraft field is within the accepted mission requirements. In space exploration, instruments measuring magnetic fields require magnetic cleanliness because it helps to distinguish the magnetic field generated by the spacecraft from the ambient field. A spacecraft has its own DC and AC magnetic fields generated by the current flow and residual moments of the spacecraft instruments. Therefore, analysis and testing are required in the development phase. The Korea Pathfinder Lunar Orbiter (KPLO) carries the KPLO Magnetometer (KMAG) instrument to observe magnetic fields on the lunar surface and the space environment around the Moon. To verify the magnetic cleanliness of the KPLO, we analyzed the magnetic field generated by it. We used the positional information and the magnetic dipole moments of twenty-five instruments, with which the KPLO is equipped. The analysis method was a Python program using magpylib. Finally, the worst-case analysis results showed that the spacecraft field around the magnetometer is less than 700 nT, which is the KMAG magnetic cleanliness requirement. [ABSTRACT FROM AUTHOR]

Compared with the traditional needle driving method, hybrid maglev needle driving is a new weft knitting machine technology, which alleviates the problems of noise, heat, and needle breakage. However, in the structure of needle arrays, magnetic disturbance between permanent magnet knitting needles leads to unstable needle control. Therefore, this paper attempts to solve this problem through a performance analysis of hybrid maglev needle driving. Based on the structure, the magnetic force distribution model of permanent magnet knitting needles is established. Aiming at the magnetic interference between magnetic arrays, a magnetic shielding material, silicon steel with a high permeability, is proposed to optimize the driving structure of a magnetic levitation needle array. Through simulation and experimental analysis, the influence of different silicon steel thicknesses on magnetic field shielding is analyzed. It is concluded that the optimal value of a silicon steel sheet is 1 mm and that the optimal gauge of hybrid maglev knitting needles is 8 mm. Finally, compared with the theoretical and simulation analysis, the experimental results have indicated that the proposed optimized structure of the gauge of hybrid maglev knitting needles is correct and effective.

The stray current of direct current (DC) railway systems causes magnetic disturbance in geomagnetic measurements, which may complicate the identification of useful information. The magnetic disturbance exhibits broadband characteristics in the frequency domain. In this paper, we propose a noise reduction method based on the adaptive Kalman filter to extract useful signals from the geomagnetic data with a high level of noise. The covariance matrixes of both the process noise (Q) and measurement noise (R) can be adaptively estimated to improve the performance of the adaptive Kalman filter. The proposed method is adopted to process the geomagnetic data collected at the Beijing Geomagnetic Observatory (BJI), which is affected by the DC railway system. The magnetic disturbance is largely reduced, and the signal-to-noise ratios of the horizontal and vertical components of the geomagnetic field are improved by more than 14 dB and 27 dB, respectively. The K-indices are calculated to evaluate the performance of the adaptive Kalman filter method. To assess the influence of the adaptive Kalman filter on natural signals, the geomagnetic data that contain rapid variations are processed. The denoising results show that the adaptive Kalman filter can effectively reduce the magnetic disturbance caused by DC railway system without large impact on the natural geomagnetic rapid variations. [ABSTRACT FROM AUTHOR]

Advancements in the micro-electromechanical systems technology have enabled the realization of small-size, high-performance inertial motion and magnetic field sensors that are embedded in most modern-day smart gadgets. These sensors, when coupled with the high-speed computing and communication technologies may potentially enable in-home monitoring and assessment of human health in the forthcoming age of Smart home technologies, internet-of-thing, and internet-of-everything. However, because the sensor’s orientation is generally arbitrary, this may cause erroneous results of important health parameters such as gait speed and range of motion of the knee joint. Therefore, it is important that the sensor’s measurements be corrected for orientation. In this work, we designed, implemented, and validated a three-stage sensor fusion algorithm. A gradient descent approach was exploited to estimate the drift in and subtract it from the cumulatively integrated gyroscope data to obtain the orientation in real time. The roll and pitch angles were obtained from the first stage, whereas the second and third stages outputs a coarse and fine estimate of yaw angle, respectively. Since the estimation was obtained primarily from the gyroscope data, the estimated orientation was least affected by the external acceleration and magnetic disturbances. The performance of the proposed algorithm was validated with a publicly available dataset, and in presence of external acceleration and magnetic disturbances. Finally, some key gait parameters were derived from the gait measurements using the proposed filter that showed high conformity to the ground-truth values. [ABSTRACT FROM AUTHOR]

The heading state of an unmanned aerial vehicle (UAV) often diverges due to magnetic field disturbances, although the magnetic field can provide information about the heading. In this paper, we propose a novel magnetic fault–tolerant navigation filter (MFTNF) for UAVs, which combines a primary filter and a magnetometer-free attitude and heading reference system (AHRS) as a secondary backup filter to obtain an accurate and robust heading under an unknown magnetic disturbance. In the MFTNF framework, the primary filter that uses the magnetic field is the main filter used in normal situations for estimating the attitude, position, and velocity states. When an anomaly in the magnetic field is detected, the magnetometer-free AHRS replaces the heading of the primary filter. To detect magnetic disturbances, a novel decision-making algorithm is proposed in this paper. Flight experiments with UAVs demonstrate that the MFTNF is accurate and reliable under an unknown magnetic disturbance and that in such circumstances, the conventional approaches typically exhibit degraded accuracy in estimating the heading and the position. In addition, the advantage of the proposed MFTNF is that it always produces a robust heading estimate, regardless of the degree of magnetic interference, due to the magnetometer-free AHRS. [ABSTRACT FROM AUTHOR]

The heading angle plays a vital role in the localization and mapping of mobile robots. It is generally obtained by fusing measurements from gyroscope and magnetometer. However, ferromagnetic objects in real-world environments will disturb the magnetic field and will, therefore, cause significant errors in the estimated heading angles. This work proposes a novel method that employs a rotating magnetometer to detect ambient spatial magnetic disturbances and corrects the heading angle. The algorithm is based on the extended Kalman filter (EKF). Firstly, a criterion named spatial disturbance index is defined to characterize the disturbance quantitatively. And then the magnetometer measurement error covariance of the EKF is tuned adaptively according to the proposed criterion, so that a relatively reliable heading angle can be obtained even under strong spatial dynamic magnetic disturbances. In addition, the estimated heading angle can quickly restore to the correct value when the spatial disturbances disappear. The proposed algorithm has the benefit of adjusting the fusing degree of gyroscope and magnetometer adaptively to reject spatial disturbances and avoid the adverse impact of inherent gyroscope drift. The algorithm is evaluated under static and dynamic conditions in real-world indoor/outdoor environments. The results show that our algorithm outperforms the conventional EKF with fixed measurement error covariance and also the algorithm using only gyroscope. [ABSTRACT FROM AUTHOR]

The results of theoretical work on addressing the effects of magnetic disturbances in the engineering development of an attitude control system of a microsatellite are presented. Magnetic disturbances were calculated using the finite element method. Their influence on the control system was evaluated by integrating numerically the differential equations of the orbital and controlled rotational motion of the spacecraft. The magnetic field of the Earth was modeled in accordance with the IGRF-12 analytical relations. [ABSTRACT FROM AUTHOR]

The paper presents an investigation of the time-dependent parameters of the solar quiet daily (Sq) variation for the minimum of the solar cycle 23/24 in 2008. We apply "Measure of Anomalousness" algorithm to detection of the magnetically quiet days. Global distribution of Sq amplitudes of the three orthogonal magnetic field components is analyzed using one-year data from 75 INTERMAGNET geomagnetic observatories and 46 SuperMAG stations. The study reveals strong latitudinal dependence of the Sq(X) amplitudes, which is approximated by the sixth-order polynomial trend curve. Sq(Y) amplitudes also suggest latitudinal dependence, while no significant difference is found in the Sq(Z) amplitudes across all latitudes. In the equatorial region, Sq(X) amplitudes are strongly affected by the equatorial electrojet (EEJ), having maxima during equinoctial seasons in the region of the South Atlantic magnetic anomaly (SAA). Sq(Y) demonstrates clear dependence on solar activity and amount of solar illumination, as the Sq(Y) amplitudes are typically greater in the summer-hemisphere and smaller in the winter-hemisphere. We analyze equivalent Sq current system using observatory data from the Australian mainland and narrow European-African latitudinal segment. Sq current system also strongly depends on solar activity, as the current vortices in the winter-hemisphere disintegrate showing no trace of Sq current loops' formation, while the current vortices become evident in the summer-hemisphere. [ABSTRACT FROM AUTHOR]

Magnetometer is a significant sensor for integrated navigation. However, it suffers from many kinds of unknown dynamic magnetic disturbances. We study the problem of online estimating such disturbances via a nonlinear optimization aided by the intermediate quaternion estimation from inertial fusion. The proposed optimization is constrained by the geographical distribution of the magnetic field forming a constrained nonlinear programming. The uniqueness of the solution has been verified mathematically, and we design an interior-point-based solver for efficient computation on embedded chips. It is claimed that the designed scheme mainly outperforms in dealing with the challenging bias estimation problem under static motion as the previous representatives can hardly achieve. Experimental results demonstrate the effectiveness of the proposed scheme on high accuracy, fast response, and low computational load. [ABSTRACT FROM AUTHOR]

This paper deals with practical issues and solutions related to the use of the giant magnetoimpedance (GMI) for electrical current measurement. It presents an overview of a full attempt of design and realization of an almost industrial GMI-based current sensor or clamp. The sensor combines dc and ac measurements with the same flexible sensitive element. This paper addresses several practical problems encountered in a real measurement environment. In this context, main attention was paid to the impact of the influence quantities that largely condition the implementation strategies and solutions of the sensor. For the intended industrial application, these influence quantities first include the operating temperature. Its impact on the intrinsic sensor response was investigated in an almost industrial temperature range. Second, bending stress on the sensitive element is another involved influence parameter in the toroidal and flexible structure of the sensor. The effect of this was also characterized. Finally, the third considered influence parameter is the effect of magnetic disturbances. These include the eccentricity of the conductor of interest and the impact of the surrounding magnetic fields such as the earth’s field or fields produced by other conductors in close proximity to the sensor. Different strategies of implementation and solutions, taking into account the impact of influence parameters, were proposed and compared. A sensor prototype was realized, and its first performances were given. [ABSTRACT FROM AUTHOR]

The attitude and heading reference system (AHRS), which consists of tri-axial magnetometer, accelerometer, and gyroscope, has been widely adopted for three-dimensional attitude determination in recent years. It provides an economical means of passive navigation that only relies on gravity and geomagnetic fields. However, despite the advantages of small size, low cost, and low power, the magnetometer and accelerometer are susceptible to external disturbances, such as the magnetic interference from nearby ferromagnetic objects and current-carrying conductors, as well as the motional acceleration of the carrier. To eliminate such disturbances, a vector-based parallel structure is introduced for the attitude filter design, which can avoid the mutual interference between gravity and geomagnetic vectors. Meanwhile, an approach to estimate and compensate the external disturbances in real time for magnetometer and accelerometer is also presented. Compared with existing designs, the proposed filter architecture and external disturbance rejection algorithm can feasibly and effectively cooperate with mainstream data fusion techniques, including complementary filter and Kalman filter. According to experiment results, in the case that large and persistent external disturbances exist, the proposed method can improve the accuracy and robustness of attitude estimation, and it outperforms the existing methods such as switching filter and adaptive filter. Furthermore, through the experiments, the critical role of fading factor in handling the external disturbance is revealed.

Heading information is critically important for autonomous mobile robots as it is necessary for scanning or sweeping predetermined areas for specific tasks. Fusing sensor data including angular rates, acceleration, and geomagnetic fields provide heading and attitude. However, the geomagnetic field is often interfered with by ferromagnetic objects or other magnetic sources, resulting in incorrect heading information. This paper describes an algorithm that detects and rejects magnetic disturbances contained in a geomagnetic field. This algorithm combined with an extended Kalman filter is implemented in a relatively low-cost, small-scale microprocessor and sensor module. The algorithm is detailed for parameters that detect magnetic disturbances. The algorithm is also evaluated outdoors by driving a mobile robot on a lawn with apparent ferromagnetic objects and on the flat roof of a ferroconcrete building that includes iron bars and electrical wires in or under the roof. The experimental results on a flat roof indicate that the algorithm improves the accuracy of the heading significantly by reducing the peak-to-peak error by 32.9% (or the rms error by 69.9%). [ABSTRACT FROM PUBLISHER]

With the advancements in micro-electromechanical systems (MEMS) technologies, magnetic and inertial sensors are becoming more and more accurate, lightweight, smaller in size as well as low-cost, which in turn boosts their applications in human movement analysis. However, challenges still exist in the field of sensor orientation estimation, where magnetic disturbance represents one of the obstacles limiting their practical application. The objective of this paper is to systematically analyze exactly how magnetic disturbances affects the attitude and heading estimation for a magnetic and inertial sensor. First, we reviewed four major components dealing with magnetic disturbance, namely decoupling attitude estimation from magnetic reading, gyro bias estimation, adaptive strategies of compensating magnetic disturbance and sensor fusion algorithms. We review and analyze the features of existing methods of each component. Second, to understand each component in magnetic disturbance rejection, four representative sensor fusion methods were implemented, including gradient descent algorithms, improved explicit complementary filter, dual-linear Kalman filter and extended Kalman filter. Finally, a new standardized testing procedure has been developed to objectively assess the performance of each method against magnetic disturbance. Based upon the testing results, the strength and weakness of the existing sensor fusion methods were easily examined, and suggestions were presented for selecting a proper sensor fusion algorithm or developing new sensor fusion method. [ABSTRACT FROM AUTHOR]

The direction of the Earth’s magnetic field is used as a reference vector to determine the heading in orientation estimation with wearable sensors. However, the magnetic field strength is weak and can be easily disturbed in the vicinity of ferromagnetic materials, which may result in inaccurate estimate of orientation. This paper presents a novel method for estimating and compensating for magnetic disturbances. The compensation algorithm is implemented within a kinematic-based extended Kalman filter and is based on an assessment of the magnetic disturbance and the change of orientation in each time step. The proposed algorithm was experimentally validated by measuring the orientation of a simple mechanical system with three degrees of freedom in an artificially disturbed magnetic field. The results of the experimental evaluation show that an Kalman filter algorithm that incorporates compensating for magnetic disturbances is capable of estimating the orientation with moderate error (the absolute median errors \Delta \widetilde \theta X,\Delta \widetilde \theta Z < {2}^{\circ } , \Delta \widetilde \theta Y < {9}^{\circ } ) when the Earth’s magnetic field is disturbed by magnetic disturbance with a magnitude equal to twice the magnitude of the Earth’s own magnetic field in different directions. [ABSTRACT FROM PUBLISHER]

Location-dependent differences of ambient magnetic fields inside a building can be used to estimate location. In this paper, an inertial/magnetic sensor is attached to a belt position and its location is estimated using the ambient magnetic field. The walking distance is estimated using the linear relationship between the walking step length and the maximum acceleration during the step. The magnetic field data during walking is compared with a pre-collected magnetic signature. In this process, calibration steps are required for two person-dependent parameters : the walking step length estimation parameter and the hard iron parameter. An adaptive algorithm is proposed, in which these person-dependent parameters are estimated in addition to the location. Thus no person-dependent parameter calibration process is required. Through experiments, it is shown that the location and parameters are estimated accurately.

This paper proposes an inertial and magnetic sensing-based parallel orientation Kalman filter (KF), which is composed of two separate KFs: one for attitude estimation with an external acceleration model as presented in author’s previous work and the other for heading estimation with a magnetic disturbance model. The novelty of the proposed attitude-heading KF, compared to other model-based KFs, is that the disturbance models are implemented by inserting them into the measurement equations without state-augmentation. In this case, there is no increase in the matrix size of the corresponding KF structure, which is advantageous as the computational cost is approximately halved when compared with the state-augmentation type approaches. The proposed KF has been experimentally verified in magnetically disturbed conditions. The proposed method has advantages over the KF with state-augmentation in the calculation efficiency and ease of use. [ABSTRACT FROM AUTHOR]

With the advancements in micro-electromechanical systems (MEMS) technologies, magnetic and inertial sensors are becoming more and more accurate, lightweight, smaller in size as well as low-cost, which in turn boosts their applications in human movement analysis. However, challenges still exist in the field of sensor orientation estimation, where magnetic disturbance represents one of the obstacles limiting their practical application. The objective of this paper is to systematically analyze exactly how magnetic disturbances affects the attitude and heading estimation for a magnetic and inertial sensor. First, we reviewed four major components dealing with magnetic disturbance, namely decoupling attitude estimation from magnetic reading, gyro bias estimation, adaptive strategies of compensating magnetic disturbance and sensor fusion algorithms. We review and analyze the features of existing methods of each component. Second, to understand each component in magnetic disturbance rejection, four representative sensor fusion methods were implemented, including gradient descent algorithms, improved explicit complementary filter, dual-linear Kalman filter and extended Kalman filter. Finally, a new standardized testing procedure has been developed to objectively assess the performance of each method against magnetic disturbance. Based upon the testing results, the strength and weakness of the existing sensor fusion methods were easily examined, and suggestions were presented for selecting a proper sensor fusion algorithm or developing new sensor fusion method.

In this study, an angular motion sensing unit using dual complementary Hall-effect (HE) sensors and interface circuit to reduce the effect of disturbance signals has been developed. The applied technique reduces the effects of disturbance signals such as strong electromagnetic field and temperature. The consequence of this technique provides a good widespread applicability in industrial and domestic applications. The unwanted disturbance signals are eliminated by analog processing to obtain a clean signal. The proposed method is based on four HE sensors in two couples with 180° distance, where the outputs of the each complementary sensor are applied to a differential amplifier. Then, the signal is changed to the logic level. An artificial turbine has been employed to revolve rotating disc in the developed angular motion sensing unit and a common magnet has been used to generate magnetic disturbance. The experimental results show that the proposed angular motion sensing unit is immune to the magnetic disturbance and able to measure the required information such as speed of rotation, direction of rotation and detect the occurred error. The power consumption is also reduced by 95.68% as compared with conventional readout circuits due to the implementation of negative logic and pulse switching technique. [ABSTRACT FROM AUTHOR]