Kalman Filter: Overview

Barnett, Glen; Zehnwirth, Ben

doi:10.1002/9781118445112.stat04585

Cited by 5 publications

(1 citation statement)

References 29 publications

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“…The Kalman Filter estimator is centered around the state vector x and its associated state covariance matrix P. A state-transition matrix F transforms this state and its covariance from one time step k to the next [33]:…”

Section: Attitude Determination 221 Kalman Filter Overviewmentioning

confidence: 99%

Characterization and Flight Test of a Multi-Antenna Gnss, Multi-Sensor Attitude Determination Algorithm

Tehrani¹

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Characterization and Flight Test of a Multi-Antenna GNSS, Multi-Sensor Attitude Determination Algorithm Nathan Tehrani A multi-antenna Global Navigation Satellite System (GNSS), multi-sensor attitude estimation algorithm is outlined, and its sensitivity to various error sources is assessed. The attitude estimation algorithm first estimates attitude using multiple GNSS antennas, and then fuses a host of other attitude estimation sensors including tri-axial magnetometers, Sun sensors, and inertial sensors. This work is motivated by the attitude determination needs of the Antarctic Impulse Transient Antenna (ANITA) experiment, a high-altitude balloon-suspended science platform. In order to assess performance trade-offs of various algorithm configurations, the attitude estimation performance of various approaches is tested using a simulation that is based on recorded ANITA III flight data. For GNSS errors, attention is focused on multipath, receiver measurement noise, and carrier-phase breaks. For the remaining attitude sensors, different grades of sensor are assessed. Through a Monte-Carlo simulation, it is shown that, under typical conditions, sub-0.1 degree attitude accuracy is available when using multiple antenna GNSS data only, but that this accuracy can degrade to degree-level in some environments warranting the inclusion of additional attitude sensors to maintain the desired level of accuracy. This algorithm was validated in a flight test. A WVU Phastball unmanned aerial vehicle was outfitted with GNSS receivers, an IMU, a magnetometer, and a Sun sensor to collect flight data. To determine the wing flex during flight, and correct the body-centric antenna coordinates, a computer vision algorithm was developed to use aircraft-mounted camera data to track markers along the wing surface and estimate the wing deflection. First and foremost, I must thank my advisor, Dr. Jason Gross, for providing the many hours of guidance, motivation, and technical assistance that made this work possible. I also thank the members of my committee, Dr. John Christian and Dr. Yu Gu, for providing the extra help and guidance that I often needed while working on this project. I must give special thanks to Scott Harper, who provided technical guidance on the Phastball Zero instrumentation. And to Stefane D'Urso for designing several of the mount points for the instruments and helping me with all aspects of the aircraft. Thank you for teaching me all that you did about the aircraft and about aircraft in general.

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Section: Attitude Determination 221 Kalman Filter Overviewmentioning

confidence: 99%