A GPS attitude error model for Kalman filtering

McMillan, J. C.

doi:10.1109/plans.1994.303333

Cited by 6 publications

(2 citation statements)

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“…This analysis used a similar method to [40], with a distinct difference in the linearization error model. Instead of using the additive noise model from (67), the linearization error is described using an unknown instrumental diagonal matrix, β k , as in (81) (99) Using this instrumental matrix, a similar analysis as [40] is used to demonstrate the stability of the UKF under certain conditions using the stochastic stability lemma. For full details of the stability proof, see [35].…”

Section: Unscented Kalman Filter Stabilitymentioning

confidence: 99%

“…The effectiveness of using multiple Global Positioning System (GPS) antennas for attitude estimation has been well established in the technical community, typically through the use of pseudorange and carrier phase signals. Different applications have been studied for multiple GPS attitude estimation including general aviation aircraft [89][90][91][92][93], ships [94][95][96][97][98][99], subscale aircraft [100,101], off-road land vehicles [102], microsatellites [103,104], general test setups [105,106], and simulation studies [107][108][109][110].…”

Section: -State Gps/ins Sensor Fusion Formulationmentioning

confidence: 99%

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Sensitivity and stability analysis of nonlinear Kalman filters with application to aircraft attitude estimation

Rhudy¹

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SENSITIVITY AND STABILITY ANALYSIS OF NONLINEAR KALMAN FILTERS WITH APPLICATION TO AIRCRAFT ATTITUDE ESTIMATION by Matthew B. Rhudy State estimation techniques are important tools for analyzing systems that contain states that are not directly measureable. If the estimated states are used, for example, in place of the true states in a feedback controller, the accuracy and stability of the estimates becomes crucial for the safe and effective execution of the controller. This is especially important in aircraft control applications, where safety is an essential concern. Because of this, the stability characteristics of the state estimation are investigated. Additionally, two different nonlinear Kalman filters are considered and compared with respect to various design parameters. This work considers the sensitivity and stability characteristics of nonlinear state estimation through the aircraft attitude estimation problem. This problem is approached using sensor information from Global Positioning System (GPS) and Inertial Navigation System (INS) in order to obtain estimates of the aircraft attitude angles. This case study uses experimentally collected flight data from subscale aircraft to derive estimation results. The goal of this work is to obtain a better understanding of the properties of nonlinear Kalman filters in order to make more informed decisions regarding the selection and tuning of these filters for different real-world applications.

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