scholarly journals Skewed-redundant Hall-effect Magnetic Sensor Fusion for Perturbation-free Indoor Heading Estimation

2020 ◽  
Author(s):  
Mojtaba Karimi ◽  
Edwin Babaians ◽  
Martin Oelsch ◽  
Tamay Aykut ◽  
Eckehard Steinbach
Keyword(s):  
Hall Effect ◽  
Sensor Fusion ◽  
Magnetic Sensor ◽  
Heading Estimation

scholarly journals Enhanced Drone Navigation in GNSS Denied Environment Using VDM and Hall Effect Sensor

2019 ◽  
Vol 8 (4) ◽  
pp. 169 ◽  
Author(s):  
Shady Zahran ◽  
Adel Moussa ◽  
Naser El-Sheimy
Keyword(s):  
Hall Effect ◽  
Low Cost ◽  
Integrated System ◽  
Global Navigation Satellite Systems ◽  
Satellite Systems ◽  
Hall Effect Sensor ◽  
Aerial Vehicle ◽  
Heading Estimation ◽  
Global Navigation Satellite ◽  
Vehicle Dynamic Model

The last decade has witnessed a wide spread of small drones in many civil and military applications. With the massive advancement in the manufacture of small and lightweight Inertial Navigation System (INS), navigation in challenging environments became feasible. Navigation of these small drones mainly depends on the integration of Global Navigation Satellite Systems (GNSS) and INS. However, the navigation performance of these small drones deteriorates quickly when the GNSS signals are lost, due to accumulated errors of the low-cost INS that is typically used in these drones. During GNSS signal outages, another aiding sensor is required to bound the drift exhibited by the INS. Before adding any additional sensor on-board the drones, there are some limitations that must be taken into considerations. These limitations include limited availability of power, space, weight, and size. This paper presents a novel unconventional method, to enhance the navigation of autonomous drones in GNSS denied environment, through a new utilization of hall effect sensor to act as flying odometer “Air-Odo” and vehicle dynamic model (VDM) for heading estimation. The proposed approach enhances the navigational solution by estimating the unmanned aerial vehicle (UAV) velocity, and heading and fusing these measurements in the Extended Kalman Filter (EKF) of the integrated system.

Full-pose estimation using inertial and magnetic sensor fusion in structurized magnetic field for hand motion tracking

Measurement ◽  
2021 ◽  
Vol 170 ◽  
pp. 108697
Author(s):  
Hui-Min Shen ◽  
Chong Lian ◽  
Xiang-Wei Wu ◽  
Feng Bian ◽  
Ping Yu ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Sensor Fusion ◽  
Pose Estimation ◽  
Motion Tracking ◽  
Magnetic Sensor ◽  
Hand Motion

scholarly journals Skewed-redundant Hall-effect Magnetic Sensor Fusion for Perturbation-free Indoor Heading Estimation

2020 ◽  
Author(s):  
Mojtaba Karimi ◽  
Edwin Babaians ◽  
Martin Oelsch ◽  
Tamay Aykut ◽  
Eckehard Steinbach
Keyword(s):  
Magnetic Field ◽  
Hall Effect ◽  
Field Measurement ◽  
Magnetic Measurement ◽  
Magnetic Sensors ◽  
Magnetic Field Measurement ◽  
Earth’S Magnetic Field ◽  
Fusion Technique ◽  
Earth's Magnetic Field ◽  
Heading Estimation

Robust attitude and heading estimation with respect to a known reference is an essential component for indoor localization in robotic applications. Affordable Attitude and Heading Reference Systems (AHRS) are typically using 9-axis solid-state MEMS-based sensors. The accuracy of heading estimation on such a system depends on the Earth's magnetic field measurement accuracy. The measurement of the Earth's magnetic field using MEMS-based magnetometer sensors in an indoor environment, however, is strongly affected by external magnetic perturbations. This paper presents a novel approach for robust indoor heading estimation based on skewed-redundant magnetometer fusion. A tetrahedron platform based on Hall-effect magnetic sensors is designed to determine the Earth's magnetic field with the ability to compensate for external magnetic field anomalies. Additionally, a correlation-based fusion technique is introduced for perturbation mitigation using the proposed skewed-redundant configuration. The proposed fusion technique uses a correlation coefficient analysis for determining the distorted axis and extracts the perturbation-free Earth's magnetic field vector from the redundant magnetic measurement. Our experimental results show that the proposed scheme is able to successfully mitigate the anomalies in the magnetic field measurement and estimates the Earth's true magnetic field. Using the proposed platform, we achieve a Root Mean Square Error of 12.74$\degree$ for indoor heading estimation without using an additional gyroscope.

scholarly journals Skewed-redundant Hall-effect Magnetic Sensor Fusion for Perturbation-free Indoor Heading Estimation

2020 ◽  
Author(s):  
Mojtaba Karimi ◽  
Edwin Babaians ◽  
Martin Oelsch ◽  
Tamay Aykut ◽  
Eckehard Steinbach
Keyword(s):  
Magnetic Field ◽  
Hall Effect ◽  
Field Measurement ◽  
Magnetic Measurement ◽  
Magnetic Sensors ◽  
Magnetic Field Measurement ◽  
Earth’S Magnetic Field ◽  
Fusion Technique ◽  
Earth's Magnetic Field ◽  
Heading Estimation

Robust attitude and heading estimation with respect to a known reference is an essential component for indoor localization in robotic applications. Affordable Attitude and Heading Reference Systems (AHRS) are typically using 9-axis solid-state MEMS-based sensors. The accuracy of heading estimation on such a system depends on the Earth's magnetic field measurement accuracy. The measurement of the Earth's magnetic field using MEMS-based magnetometer sensors in an indoor environment, however, is strongly affected by external magnetic perturbations. This paper presents a novel approach for robust indoor heading estimation based on skewed-redundant magnetometer fusion. A tetrahedron platform based on Hall-effect magnetic sensors is designed to determine the Earth's magnetic field with the ability to compensate for external magnetic field anomalies. Additionally, a correlation-based fusion technique is introduced for perturbation mitigation using the proposed skewed-redundant configuration. The proposed fusion technique uses a correlation coefficient analysis for determining the distorted axis and extracts the perturbation-free Earth's magnetic field vector from the redundant magnetic measurement. Our experimental results show that the proposed scheme is able to successfully mitigate the anomalies in the magnetic field measurement and estimates the Earth's true magnetic field. Using the proposed platform, we achieve a Root Mean Square Error of 12.74$\degree$ for indoor heading estimation without using an additional gyroscope.

scholarly journals Enhanced Vehicle Identification Utilizing Sensor Fusion and Statistical Algorithms

2009 ◽  
Author(s):  
Stephane Roussel ◽  
Hemanth Porumamilla ◽  
Charles Birdsong ◽  
Peter Schuster ◽  
Christopher Clark
Keyword(s):  
Sensor Fusion ◽  
Partial Information ◽  
Probabilistic Approach ◽  
Measurement Data ◽  
Traffic Monitoring ◽  
Magnetic Sensor ◽  
Statistical Techniques ◽  
Vehicle Identification ◽  
Statistical Algorithms ◽  
The Individual

Several studies in the area of vehicle detection and identification involve the use of probabilistic analysis and sensor fusion. While several sensors utilized for identifying vehicle presence and proximity have been researched, their effectiveness in identifying vehicle types has remained inadequate. This study presents the utilization of an ultrasonic sensor coupled with a magnetic sensor and the development of statistical algorithms to overcome this limitation. Mathematical models of both the ultrasonic and magnetic sensors were constructed to first understand the intrinsic characteristics of the individual sensors and also to provide a means of simulating the performance of the combined sensor system and to facilitate algorithm development. Preliminary algorithms that utilized this sensor fusion were developed to make inferences relating to vehicle proximity as well as type. It was noticed that while it helped alleviate the limitations of the individual sensors, the algorithm was affected by high occurrences of false positives. Also, since sensors carry only partial information about the surrounding environment and their measured quantities are partially corrupted with noise, probabilistic techniques were employed to extend the preliminary algorithms to include these sensor characteristics. These statistical techniques were utilized to reconstruct partial state information provided by the sensors and to also filter noisy measurement data. This probabilistic approach helped to effectively utilize the advantages of sensor fusion to further enhance the reliability of inferences made on vehicle identification. In summary, the study investigated the enhancement of vehicle identification through the use of sensor fusion and statistical techniques. The algorithms developed showed encouraging results in alleviating the occurrences of false positive inferences. One of the several applications of this study is in the use of ultrasonic-magnetic sensor combination for advanced traffic monitoring such as smart toll booths.

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