Inertial navigator error models for large heading uncertainty

“…Nowadays, we are used to the attitude approximation by three one-dimension error angles. For example, many works have been devoted to the nonlinear angle error models to account for large heading uncertainty [4,8,9]. By so doing, most inherent characteristics of the three-dimension attitude have been lost.…”

mentioning

confidence: 99%

Velocity/Position Integration Formula Part I: Application to In-Flight Coarse Alignment

Pan

2013

IEEE Trans. Aerosp. Electron. Syst.

209

133

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Velocity/Position Integration Formula (I): Application to In-flight Coarse Alignment 2 roughly known initial attitude, otherwise they cannot guarantee a rapid and accurate alignment result [1][2][3][4][5]. If the SINS is stationary or quasi-stationary, analytic methods are often used to derive a coarse attitude from gyroscope/accelerometer measurements [1,3,6]. The heading angle is more difficult to determine than the two level angles and for a consumer grade SINS, is usually aided by a magnetic compass. The in-motion or in-flight alignment is necessary for many military applications and commercial aviations [7]. In such cases, the SINS is in motion, e.g., onboard a ship or aircraft, the direction of velocity/trajectory from an aided source, such as GPS, can provide a rough pitch and heading angles during a straight course. This information is generally not good enough to perform a reliable fine alignment due to the water current and air speed [3], let alone the SINS misaligning angles relative to the carrier.It may be argued that the coarse alignment difficulty confronting the navigation field is largely owed to our "local eye" on the attitude representation in three-dimension space. Nowadays, we are used to the attitude approximation by three one-dimension error angles. For example, many works have been devoted to the nonlinear angle error models to account for large heading uncertainty [4,8,9]. By so doing, most inherent characteristics of the three-dimension attitude have been lost. Our group proposed a recursive alignment approach based on attitude optimization in [10], which, for the first time in the public literature, transforms the attitude alignment problem into a continuous attitude determination problem [11] using infinite vector observations. It was rigorously proven therein that the behavior of the estimated constant initial angles can be used to detect significant sensor biases. The optimization approach is related to the so-called inertial frame method [12,13], but more theoretically solid and more robust to disturbances and noise, because it makes full use of the special algebraic property of the attitude matrix (a three-dimension orthogonal matrix with unit determinant). If the nonzero velocity rate information was externally provided, the

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“…with δ θ θ θ the misalignment vector of computer c-frame [3] with respect to l-frame as a consequence of position error:…”

Section: A Error Constructionmentioning

confidence: 99%

Study of MEMS-based inertial sensors operating in dynamic conditions

Stebler

Guerrier

Skaloud

et al. 2014

2014 IEEE/ION Position, Location and Navigation Symposium - PLANS 2014

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Abstract-This paper aims at studying the behaviour of the errors coming from inertial sensors when measured in dynamic conditions. After proposing a method for constructing the error process, the properties of these errors are estimated via the Generalized Method of Wavelets Moments methodology. The developed model parameters are compared to those obtained under static conditions. Finally an attempted is presented to find the link between the encountered dynamic of the vehicle and error-model parameters.

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Inertial navigator error models for large heading uncertainty

Cited by 80 publications

References 3 publications

New techniques for initial alignment of strapdown inertial navigation system

New techniques for initial alignment of strapdown inertial navigation system

Velocity/Position Integration Formula Part I: Application to In-Flight Coarse Alignment

Study of MEMS-based inertial sensors operating in dynamic conditions

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