Academic literature on the topic 'Magnetic heading'

For land navigation applications, the integration of the magnetometer with the combination of MEMS-INS and the Global Navigation Satellite System (GNSS) give excellent results. During land navigation applications, the magnetometer’s heading can also be used during the GNSS outages. The calibration of the magnetometer is indispensable to calculate its accurate heading. There exist several methods for magnetometer calibration. Some are offline and some are online calibration techniques. In this paper, a calibration method is proposed to estimate the magnetometer’s parameters through online calibration in run time. In this method, the reference magnetic field is calculated from the World Magnetic Model (WMM-2020). Moreover, reference roll, pitch and heading are provided from some other sources such as GNSS, Attitude Heading Reference System (AHRS), or reference INS. For different roll and pitch sectors, calibration parameters are estimated and stored. These parameters are used for magnetometer online calibration during the field testing. Both the headings obtained by the online calibration and conventional lab calibrations are analysed. Furthermore, the heading estimated through the online calibration is autonomous and fast. Subsequently, there is no user involvement in this online calibration technique and no specific movements to the device are provided. The heading obtained by novel technique is as accurate as obtained by conventional offline lab calibration.

Abstract The three-component magnetic sensor is used to measure the heading of the vehicle. Under working, the magnetic sensor be rotated to the horizontal plane according to the attitude angle of the vehicle, and then the heading estimation is performed. However, the vehicle’s attitude angle measurement errors affect the accuracy of the magnetic sensor measurement’s east and north components and then lead to a false heading result. Based on the relation of magnetic field measurement error and magnetic heading error, the effect model of geomagnetic measurement error on magnetic heading error is derived in this paper. Finally, the characteristics of attitude angle errors on magnetic heading are verified by experiments, and the results show that particular angles can avoid the magnetic compass’s effect by the vehicle’s attitude angle error.

Measurement method is studied utilizing vertical gyroscopevertical gyroscopevertical gyroscope, static earth magnetic field sensor and CPU. According to electromagnetism induction law, when a wire loop incises the magnetic force line of the earth magnetic field, it will bring about the induction electromotive force, the earth magnetic field can be calculated by signal processing of induced voltage in measuring coil. A heading and attitude measuring system to vehicle are designed, the heading precision were raised. Experiment data states clearly the heading precision is not more than 0.5°. This system can be used to detect heading and attitude, and can be extended easily.

An accurate heading solution is required for many applications and it can be achieved by high grade (high cost) gyroscopes (gyros) which may not be suitable for such applications. Micro-Electro Mechanical Systems-based (MEMS) is an emerging technology, which has the potential of providing heading solution using a low cost MEMS-based gyro. However, MEMS-gyro-based heading solution drifts significantly over time. The heading solution can also be estimated using MEMS-based magnetometer by measuring the horizontal components of the Earth magnetic field. The MEMS-magnetometer-based heading solution does not drift over time, but are contaminated by high level of noise and may be disturbed by the presence of magnetic field sources such as metal objects. This paper proposed an accurate heading estimation procedure based on the integration of MEMS-based gyro and magnetometer measurements that correct gyro and magnetometer measurements where gyro angular rates of changes are estimated using magnetometer measurements and then integrated with the measured gyro angular rates of changes with a robust filter to estimate the heading. The proposed integration solution is implemented using two data sets; one was conducted in static mode without magnetic disturbances and the second was conducted in kinematic mode with magnetic disturbances. The results showed that the proposed integrated heading solution provides accurate, smoothed and undisturbed solution when compared with magnetometerbased and gyro-based heading solutions.

In the view of the disadvantages brought by DMC(digital magnetic compass) and gyro when they are used separately, a combined directional instrument composed by a 3-axes digital magnetic compass and a 3-axes MEMS gyro for vehicles is introduced, which is less influenced to the magnetic field interference and random drift. In the abnormal magnetic field, the MEMS gyro is used for correcting the heading angle of DMC. The experiment data shows that this instrument can output a stable and high-precision heading angle. It can be used to provide heading angle for vehicles.

Robust attitude and heading estimation in an indoor environment with respect to a known reference are essential components for various robotic applications. Affordable Attitude and Heading Reference Systems (AHRS) are typically using low-cost solid-state MEMS-based sensors. The precision of heading estimation on such a system is typically degraded due to the encountered drift from the gyro measurements and distortions of the Earth’s magnetic field sensing. This paper presents a novel approach for robust indoor heading estimation based on skewed redundant inertial and magnetic sensors. Recurrent Neural Network-based (RNN) fusion is used to perform robust heading estimation with the ability to compensate for the external magnetic field anomalies. We use our previously described correlation-based filter model for preprocessing the data and for empowering perturbation mitigation. Our experimental results show that the proposed scheme is able to successfully mitigate the anomalies in the saturated indoor environment and achieve a Root-Mean-Square Error of less than [Formula: see text] for long-term use.

Magnetic sensors have been used in navigation for many centuries. During this time the effects of magnetic interference from ferromagnetic materials used in vehicle construction have become an increasing problem, and correction techniques have evolved progressively to allow the continued use of magnetic heading detection. The advent of the microprocessor has made it possible to take a fresh look at the problems of compass operation in vehicles and to devise more accurate processes for the correction of the indicated heading. The compass system described in this paper uses a mathematical representation of the magnetic environment based on fundamental physical principles to supply accurate heading information even in the most magnetically hostile land vehicles.

To analyze the characteristics of the aeromagnetic scalar gradient detection method, a uniformly magnetized ellipsoid is used to simulate an unexploded ordnance, and a magnetic field detection model is established in the International Geomagnetic Reference Field based on rotation matrices. Furthermore, the spatial distribution of the target’s magnetic field is simulated. The results indicate that the scalar gradient detection curve is closely related to the unmanned aerial vehicle (UAV) heading, geomagnetic direction, and target attitude. According to the measured data, the aeromagnetic detection system exhibits differences in the detection of different headings, indicating that some “blind areas” exist in the scalar gradient magnetic detection method. The experimental measurement by a quadrotor UAV equipped with two optical pump magnetometers verifies that the scalar gradient detection method can effectively eliminate the geomagnetic field as well as the interferences of the UAV itself. Furthermore, the angular relationship between the target magnetic field contour distribution and the heading is found to be the main reason that the scalar gradient detection system enters the “blind detection area.” Therefore, a flight strategy of “positive direction + orthogonal grid” is proposed. This method effectively reduces the missed detection rate of scalar gradient detection and provides strategic guidance for the detection path of aeromagnetic scalar gradient system.

La localisation d'une personne ou d'un bien dans des environnements intérieurs est devenue une nécessité. Le système de positionnement par satellites, une solution prédominante pour la localisation en extérieur, rencontre des limites lorsqu'il est appliqué en intérieur en raison de la réflexion des signaux et de l'atténuation causée par les obstacles. Pour y remédier, diverses solutions de localisation en intérieur ont été étudiées. Les méthodes de localisation en intérieur sans fil exploitent les signaux pour déterminer la position d'un appareil dans un environnement intérieur. Cependant, l'interférence des signaux, souvent causée par des obstacles physiques, des réflexions et des appareils concurrents, peut entraîner des imprécisions dans l'estimation de la position. De plus, ces méthodes nécessitent le déploiement d'infrastructures, ce qui entraîne des coûts d'installation et de maintenance. Une autre approche consiste à estimer le mouvement de l'utilisateur à l'aide des capteurs inertiels de l'appareil. Toutefois, cette méthode se heurte à des difficultés liées à la précision des capteurs, aux caractéristiques de mouvement de l'utilisateur et à la dérive temporelle. D'autres techniques de localisation en intérieur exploitent les champs magnétiques générés par la Terre et les structures métalliques. Ces techniques dépendent des appareils et des capteurs utilisés ainsi que de l'environnement dans lequel se situe l'utilisateur.L'objectif de cette thèse est de réaliser un système de localisation en intérieur conçu pour les professionnels, tels que les pompiers, les officiers de police et les travailleurs isolés, qui ont besoin de solutions de positionnement précises et robustes dans des environnements intérieurs complexes. Dans cette thèse, nous proposons un système de localisation en intérieur qui exploite les récentes avancées en vision par ordinateur pour localiser une personne à l’intérieur d’un bâtiment. Nous développons un système de localisation au niveau de la pièce. Ce système est basé sur l'apprentissage profond et les capteurs intégrés dans le smartphone, combinant ainsi les informations visuelles avec le cap magnétique du smartphone. Pour se localiser, l'utilisateur capture une image de l'environnement intérieur à l'aide d'un smartphone équipé d'une caméra, d'un accéléromètre et d'un magnétomètre. L'image capturée est ensuite traitée par notre système composé de plusieurs réseaux neuronaux convolutionnels directionnels pour identifier la pièce spécifique dans laquelle se situe l'utilisateur. Le système proposé nécessite une infrastructure minimale et fournit une localisation précise. Nous soulignons l'importance de la maintenance continue du système de localisation en intérieur par vision. Ce système nécessite une maintenance régulière afin de s'adapter à l'évolution des environnements intérieurs, en particulier lorsque de nouvelles pièces doivent être intégrées dans le système de localisation existant. L'apprentissage incrémental par classe est une approche de vision par ordinateur qui permet aux réseaux neuronaux profonds d'intégrer de nouvelles classes au fil du temps sans oublier les connaissances déjà acquises. Dans le contexte de la localisation en intérieur par vision, ce concept doit être appliqué pour prendre en compte de nouvelles pièces. La sélection d'échantillons représentatifs est essentielle pour contrôler les limites de la mémoire, éviter l'oubli et conserver les connaissances des classes déjà apprises. Nous développons une méthode de sélection d'échantillons basée sur la cohérence pour l'apprentissage incrémental par classe dans le cadre de l'apprentissage profond. La pertinence de la méthodologie et des contributions algorithmiques de cette thèse est rigoureusement testée et validée par des expérimentations et des évaluations complètes sur des données réelles
The need to determine the location of individuals or objects in indoor environments has become an essential requirement. The Global Navigation Satellite System, a predominant outdoor localization solution, encounters limitations when applied indoors due to signal reflections and attenuation caused by obstacles. To address this, various indoor localization solutions have been explored. Wireless-based indoor localization methods exploit wireless signals to determine a device's indoor location. However, signal interference, often caused by physical obstructions, reflections, and competing devices, can lead to inaccuracies in location estimation. Additionally, these methods require access points deployment, incurring associated costs and maintenance efforts. An alternative approach is dead reckoning, which estimates a user's movement using a device's inertial sensors. However, this method faces challenges related to sensor accuracy, user characteristics, and temporal drift. Other indoor localization techniques exploit magnetic fields generated by the Earth and metal structures. These techniques depend on the used devices and sensors as well as the user's surroundings.The goal of this thesis is to provide an indoor localization system designed for professionals, such as firefighters, police officers, and lone workers, who require precise and robust positioning solutions in challenging indoor environments. In this thesis, we propose a vision-based indoor localization system that leverages recent advances in computer vision to determine the location of a person within indoor spaces. We develop a room-level indoor localization system based on Deep Learning (DL) and built-in smartphone sensors combining visual information with smartphone magnetic heading. To achieve localization, the user captures an image of the indoor surroundings using a smartphone, equipped with a camera, an accelerometer, and a magnetometer. The captured image is then processed using our proposed multiple direction-driven Convolutional Neural Networks to accurately predict the specific indoor room. The proposed system requires minimal infrastructure and provides accurate localization. In addition, we highlight the importance of ongoing maintenance of the vision-based indoor localization system. This system necessitates regular maintenance to adapt to changing indoor environments, particularly when new rooms have to be integrated into the existing localization framework. Class-Incremental Learning (Class-IL) is a computer vision approach that allows deep neural networks to incorporate new classes over time without forgetting the knowledge previously learned. In the context of vision-based indoor localization, this concept must be applied to accommodate new rooms. The selection of representative samples is essential to control memory limits, avoid forgetting, and retain knowledge from previous classes. We develop a coherence-based sample selection method for Class-IL, bringing forward the advantages of the coherence measure to a DL framework. The relevance of the methodology and algorithmic contributions of this thesis is rigorously tested and validated through comprehensive experimentation and evaluations on real datasets

碩士
國立臺灣海洋大學
輪機工程系
94
Abstract For hundreds of years now ships have capitalized on the field generated by the earth’s magnetic poles to navigate around the world. The crude compass has evolved into a magnetic sensing device commonly know as a magnetometer. This directional tool has been used very successfully as a heading and orientation sensor on dynamic platforms. Magnetometers are sensor that detect both the signal and magnitude of the earth field as a voltage output. Using solid-state magnetic sensors and a tilt sensor , a low coast compass system can be realized. This paper covers part of the design vessel heading control systems. The paper presents the principle and design of magnetic sensors in direction control. HMR 3300 is a 3-axial magnetometer, that uses the RS232 converter. This paper presents the design method of the RS232 signal generator based on the programming language Visual C++. And it can generate different basic signals and chirp signals. It has a variety of functions including display of the signal for heading and linking another vessel electrical system .

Due to the limitation of sensor application in special environment such as relatively closed and magnetic interference environment, the positioning and the heading angle errors of wall climbing robots accumulate with time. This paper proposes a difference projection localization method based on an external RGB-D camera and a robot-carried inertial measurement unit (IMU). We differential the depth image to obtain the distance change due to the robot occupancy. Then, the 3D point cloud information is converted into 2D image information by projecting the above distances along the normal vector of the robot chassis, which greatly speeds up the computational speed. The position of the robot is calculated by studying the statistical characteristics of the projection. Two EKFs are designed to estimate the attitude, taking the gravity vector and the normal vector of the robot chassis as observation. The experimental results show that the localization error of the wall climbing robot is within 0.017m, and the heading angle error of the attitude estimation is within 3.1∘. The obtained results prove its applicability in self-localization of the wall climbing robot.

We present first-standards estimations on Fe, Nd, and SmCo5 utilizing the self-predictable maximum capacity linearized increased plane wave (FPLAPW) strategy. The attractive snapshots of Fe, Nd, and Smco5 were determined utilizing the WIEN2K code. The minutes for BCC Fe and HCP Nd are 2.27μB and 2.65μB separately in great concurrence with test esteems. For Smco5, we efficiently study the impact of considering the twist circle coupling and Coulomb connections in the Sm f shell on the attractive properties, electronic construction, and twist thickness maps. The determined attractive second and magneto-crystalline anisotropy like anisotropy are in acceptable concurrence with test esteems. The twist thickness maps in the (001) plane show that the impact of the twist circle coupling on the twist thickness design of Sm particles is more grounded than that of Coulomb connection. We additionally study the impact of the polarization heading on the energy groups by looking at the highlights of band structure when the charge bearing is along or opposite to the c-axis. The determined outcomes are in acceptable concurrence with the exploratory information.

Abstract The natural geometry of electromagnetic fields is four-dimensional in space and time. In the previous chapters we have developed a mathematical structure to deal with four-dimensional electromagnetic effects. We have also shown that the more familiar three-dimensional quantities can be obtained as projections from four-dimensional space. In principle, therefore, it is always possible to treat electromagnetic problems by starting with the full four dimensional geometry and then making suitable approximations. However, this is not the best procedure, because various approximations are important in different technological applications and each of these requires close attention to its own peculiar geometrical features. As a result it is convenient to consider the subject under such headings as quasi-static electric or magnetic fields, diffusion of energy in conducting materials, magnetic induction fields, and electromagnetic radiation. In this chapter we consider radiation and show that a full understanding of radiation phenomena needs the covariant relativistic formalism which we have developed.