The Seven Ways to Find Heading

Gade, Kenneth

doi:10.1017/s0373463316000096

Cited by 68 publications

(32 citation statements)

References 17 publications

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“…using a particle filter and terrain information or odometry [24], or by using feature matching [3,Chapter 11]. For obtaining the heading of the ground station antenna, there are also a variety of options [25], notably [26] where attitude is determined from a camera and a digital surface model with a standard deviation of 0.018 • in heading.…”

Section: Calibrationmentioning

confidence: 99%

Field Test Results of GNSS-denied Inertial Navigation aided by Phased-array Radio Systems for UAVs

Gryte¹,

Bryne²,

Albrektsen³

et al. 2019

2019 International Conference on Unmanned Aircraft Systems (ICUAS)

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Unmanned aerial vehicles (UAVs) often depend on global navigation satellite systems (GNSS) and magnetic compasses for navigation, making them exposed to malicious attacks and sensitive to magnetic anomalies, while restricting operations to within GNSS coverage. By rather relying on inertial navigation aided by spherical position measurements from a phased-array radio system (PARS), these vulnerabilities are avoided. The navigation system relies on a multiplicative extended Kalman filter for state corrections, and on outlier rejection to mitigate effects of radio reflections. Field testing shows that, despite the higher levels of noise in the PARS signal, the PARS-based position, velocity and attitude estimates are satisfactory when compared to the autopilot based attitude and velocity and the real-time kinematic (RTK) GNSS position reference solution.

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Section: Calibrationmentioning

confidence: 99%

Field Test Results of GNSS-denied Inertial Navigation aided by Phased-array Radio Systems for UAVs

Gryte¹,

Bryne²,

Albrektsen³

et al. 2019

2019 International Conference on Unmanned Aircraft Systems (ICUAS)

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“…In this regard, attitude estimation refers to the angular measurement on the vertical plane, with respect to the local level frame, and is computed as inclinations. Similarly, angular measurement on the horizontal plane, with respect to the geographical true north, is referred as heading angle (also known as yaw or azimuth) [12].…”

Section: Introductionmentioning

confidence: 99%

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

Karimi¹,

Babaians²,

Oelsch³

et al. 2020

Preprint

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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.

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“…Other applications however, such as georeferencing, photogrammetry and mapping, require more accurate state estimates. It is especially the accurate estimation of the vehicle heading which is a challenge [3]. For UAVs flying in a steady state, or hovering, with low acceleration and angular rate, the errors in attitude and IMU biases are not observable with only a single GNSS antenna, and the estimate will rely on the magnetic compass.…”

Section: Introductionmentioning

confidence: 99%

“…For UAVs flying in a steady state, or hovering, with low acceleration and angular rate, the errors in attitude and IMU biases are not observable with only a single GNSS antenna, and the estimate will rely on the magnetic compass. Magnetometers are susceptible to disturbances from irregularities in the Earth's magnetic field, or ferrous materials or electrical currents close to the sensor [3]. They are also not very useful when navigating near the magnetic poles, and the local magnetic declination values for the areas of operation must be known.…”

Section: Introductionmentioning

confidence: 99%

Pose Estimation of UAVs Based on INS Aided by Two Independent Low-Cost GNSS Receivers

Sollie

Bryne

Johansen

2019

2019 International Conference on Unmanned Aircraft Systems (ICUAS)

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Increasing use of UAVs in high-precision applications, such as georeferencing and photogrammetry, increases the requirements on the accuracy of the estimated position, velocity and attitude of the vehicle. Commercial systems that utilize magnetometers in the heading estimates are cheap, but are affected by disturbances from both the vehicle itself, nearby metal structures and variations in the Earth's magnetic field. On the other side, commercial dual-antenna satellite navigation systems can provide the required accuracy, but are expensive. This paper explores the use of a low-cost setup using two independent GNSS receivers, aiding an inertial navigation system by using pseudorange, Doppler frequency and carrier phase measurements from two longitudinally separated receivers on a fixed-wing UAV. The sensor integration was based on a multiplicative extended Kalman filter (MEKF). The main contribution of this paper is the derivation of measurement models for the raw GNSS measurements based on the MEKF error state, taking into account antenna lever arms and explicitly including the difference in measurement time between the receivers in the measurement model for double differenced carrier phase. The proposed method is verified using data collected from a UAV flight.

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The Seven Ways to Find Heading

Cited by 68 publications

References 17 publications

Field Test Results of GNSS-denied Inertial Navigation aided by Phased-array Radio Systems for UAVs

Field Test Results of GNSS-denied Inertial Navigation aided by Phased-array Radio Systems for UAVs

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

Pose Estimation of UAVs Based on INS Aided by Two Independent Low-Cost GNSS Receivers

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