An Inexpensive DME-Aided Dead Reckoning Navigator

Gebre-Egziabher, Demoz; Boyce, C.O. Lee; Powell, J. David; Enge, Per

doi:10.1002/j.2161-4296.2003.tb00333.x

Cited by 6 publications

(6 citation statements)

References 5 publications

(8 reference statements)

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“…Both terms cover a range of technologies from cheap but inaccurate microelectromechanical systems (MEMS) systems that operate by measuring vibrations to optical system that operate by measuring the Coriolis effect on a circular light tunnel. 1,4 As pointed out by Gebre-Egziabher, 4 a single error model is unlikely to be equally accurate across all such types of devices, and a simple error model is unlikely to be effective in long-term simulations. In particular, the model we use here does not account for factors like Schuler oscillations, cross-axis sensitivity, calibration errors, and a host of other factors.…”

Section: Imu Noisementioning

confidence: 99%

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Sources of Error in GPS-Denied Odometry

Brody,

Leung,

Shamwell

et al. 2023

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Section: Imu Noisementioning

confidence: 99%

“…Table 1 is reproduced from Gebre-Egziabher. 4 It is worth noting that the slopes are not unique, and thus differing noise sources cannot always be distinguished purely on the basis of Allan variance.…”

Section: Imu Noisementioning

confidence: 99%

Sources of Error in GPS-Denied Odometry

Brody,

Leung,

Shamwell

et al. 2023

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show abstract

“…Bias is a constant offset from the nominal sensor signal statistics, while drift is a time-varying offset from the nominal statistics of the sensor signal, and scaling factor is a factor scaling the signal value where the waveform itself does not change. 16 These errors are generally categorized into two categories: deterministic errors and stochastic errors. Constant biases and scaling factors are in deterministic group which are called calibration errors, while drifts are stochastic errors which can be modeled by a Gauss–Markov process with an additive wide-band noise.…”

Section: Problem Formulation and Theoretical Backgroundmentioning

confidence: 99%

“…They are augmented to motion state parameters and estimated through state estimation methods, however; calibration parameters are estimated using parameter estimation methods in a separate estimation module. 17–19 In this article, it is concentrated on calibration errors and the readers are referenced to Gebre-Egziabher, 16 Sadeghzadeh-Nokhodberiz et al, 19 and Flenniken et al 20 to find fault detection approaches for other sources of errors. Considering calibration parameters in the measurement, the accelerometer measurement model is formulated as follows…”

Section: Problem Formulation and Theoretical Backgroundmentioning

confidence: 99%

Interconnected maximum likelihood estimator and extended Kalman filter for inertial measurement unit calibration fusing three-dimensional camera information

Sadeghzadeh-Nokhodberiz

Poshtan

2014

Proceedings of the Institution of Mechanical Engineers, Part I:

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This article presents a method for calibration of inertial sensors (gyroscopes and accelerometers). In the proposed self-calibration method, interacted extended Kalman filter and maximum likelihood estimator are applied for estimation of motion state parameters and calibration parameters (biases and scaling factors), respectively. The extended Kalman filter is employed to estimate nonlinear Gaussian attitude kinematics fusing three-dimensional camera information given the estimated calibration parameters. These parameters are estimated in maximum likelihood–based system identification module and transferred to the state estimation module to estimate motion parameters. The calibration square error is then formulated and the effect of model mismatch is evaluated. The approach takes advantage of modular design, sensor fusion, and self-calibration. Self-calibration makes the method economically efficient because it does not rely on using a mechanical platform rotating the inertial measurement unit into different precisely controlled orientations. Experimental results based on data from a three-dimensional microelectromechanical system–based inertial measurement unit and a three-dimensional camera system are used to demonstrate the efficiency of the method. Comparisons are also provided to compare the method with state-of-the-art approaches.

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“…Another important factor affecting INS performance is the accuracy of local gravity information. A popular model (Titterton and Weston, 2004) (Gebre-Egziahber, 2001) constituting of the deflections of vertical (DOV) and vertical gravity anomaly is used in this work and the error analysis is shown as:where δ h is the vertical position error, δξ and δη are the DOV errors in north-south and east-west directions respectively. g 0 and R 0 are the nominal values of local gravity and the earth's radius.…”

Section: Individual Gps and Ins System Error Modelsmentioning

confidence: 99%

High-Integrity GPS/INS Integrated Navigation with Error Detection and Application to LAAS

Chan

Pervan

2011

J. Navigation

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A dynamic state realization for tightly coupling Global Positioning System (GPS) measurements with an Inertial Navigation System (INS) is described. The realization, based on the direct fusion of GPS and INS systems through Kalman filter state dynamics, explicitly accounts for temporal and spatial decorrelation of GPS measurement errors (such as tropospheric, ionospheric, and multipath errors) through state augmentation, thereby ensuring Kalman filter integrity under fault-free error conditions. Predicted system performance for a Local Area Augmentation System (LAAS) aircraft precision approach application is evaluated using covariance analysis and validated with flight data.Built-in fault detection mechanisms based on the Kalman filter innovations are also evaluated to help provide integrity under certain fault conditions. It is shown that an algorithm based on the integral of Kalman filter innovations outperforms other existing GPS fault detection methods in the detection of slowly developing ranging errors, such as those caused by ionospheric and tropospheric anomalies.

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An Inexpensive DME-Aided Dead Reckoning Navigator

Cited by 6 publications

References 5 publications

Sources of Error in GPS-Denied Odometry

Sources of Error in GPS-Denied Odometry

Interconnected maximum likelihood estimator and extended Kalman filter for inertial measurement unit calibration fusing three-dimensional camera information

High-Integrity GPS/INS Integrated Navigation with Error Detection and Application to LAAS

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