GPS Fault Detection with IMU and Aircraft Dynamics

Bruggemann, Troy S.; Greer, Duncan G.; Walker, Rodney A.

doi:10.1109/taes.2011.5705677

Cited by 33 publications

(22 citation statements)

References 9 publications

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“…In recent years, several authors have contributed to this research: Julier and Durrant-Whyte theoretically analyzed the combined filter structure as used by Koifman and Bar-Itzhack ( Figure 1) and confirmed that it optimally combines the information of both models [12]. The value of vehicle dynamics in navigation systems has been demonstrated in simulation [13][14][15] and experiments [16][17][18][19][20] with different vehicle platforms. Furthermore, new filter structures have been proposed, all of which have state vectors of reduced sizes compared to [11].…”

Section: Related Workmentioning

confidence: 91%

“…This allows the elimination of duplicate states for the navigation solution while preserving mathematical equivalence. Instead of calculating different models and then applying a pseudomeasurement as is done in [11,14,15], all these steps are merged in the prediction step of the Unified Model as depicted in Figure 3. Because this finding is of general nature, and not specific to the application in model-aided navigation, we will first derive the Unified Model method from the combined filter method [11,12] for two prediction models in a general fashion.…”

Section: Overview Of the Unified Model Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Unified Model Technique for Inertial Navigation Aided by Vehicle Dynamics Model

et al. 2013

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Model‐aided navigation increases navigation accuracy by including a vehicle dynamics model into the filter structure. The commonly used Inertial Navigation System (INS) is hence supplemented by another prediction model for the system state. However, the standard Kalman filter only allows for a single system model to propagate the estimation. The main contribution of this paper is the improvement of an existing approach to estimation with two valid state prediction models. By unifying the models, computation time and state vector size are reduced. Furthermore, the question of how the models must be coupled to achieve optimality and preserve filter stability is addressed.In integrated aircraft navigation, an INS as well as a vehicle dynamics model are available. The presented method unifies these two models and shows superior computational performance compared to existing model‐aided navigation methods and among best results. Furthermore, it is easy to implement and easy to extend with aiding sensors. Copyright © 2013 Institute of Navigation.

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Section: Related Workmentioning

confidence: 91%

Section: Overview Of the Unified Model Methodsmentioning

confidence: 99%

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Unified Model Technique for Inertial Navigation Aided by Vehicle Dynamics Model

et al. 2013

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“…However, such integrated systems should be evaluated with respect to both accuracy and computational requirements [22], e.g., the gyro applied in [23] with a noise level of 0.45 deg/s that is much larger than the error caused by the triple-cross-product term in Table II (3.4398e-7 rad/s, which is equivalent to 1.9710e-5 deg/s or 0.071 deg/h). Hence, the error caused by the triple-cross-product term can be ignored in such an integrated system where the gyro sensor accuracy is low.…”

Section: Other Aspectsmentioning

confidence: 99%

High-order attitude compensation in coning and rotation coexisting environment

Wang

et al. 2015

IEEE Trans. Aerosp. Electron. Syst.

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With the development of high-accuracy inertial navigation system inertial sensors, such as ring laser gyroscopes and atomic spin gyroscopes, it is increasingly important to improve the strapdown inertial navigation algorithms to match such high-accuracy inertial sensors. For example, the existing inertial navigation algorithms have not taken into account the triple-cross-product term of noncommutativity error. However, theoretical analysis demonstrates that the ignored triple-cross-product term is nonignorable under coning motion with constant angular rate precession environments. In this paper, a new high-accuracy rotation vector algorithm is proposed for strapdown inertial navigation. The Taylor series about time is used for error analysis and optimization of the new algorithm. General maneuvers and coning motion with constant angular rate precession environments are considered in establishing the coefficient equations in the proposed algorithm. Error drift equations are given after the error compensation. Normalized quaternion under coning motion with constant angular rate precession environments and Savage's severe integrated angular-rate profiles are used to numerically verify the new algorithm. The results Manuscriptindicate that the new high-order attitude updating algorithm can improve inertial navigation accuracy.NOMENCLATURE δφ c (t) = coning integral over the time interval from t m−1 to t δφ hA (t) = the integral of the triple-cross product noncommutativity rate vector over the time interval from t m−1 to t related to the coning motion, which is called the triple-cross-product term part A δφ hB (t) = the integral of the triple-cross-product noncommutativity rate vector over the time interval from t m−1 to t related to the high dynamic motion, which is called the triple-cross-product term part B δφ hA (t) = algorithms for the triple-cross-product term part A δφ hB (t) = algorithms for the triple-cross-product term part B δφ hA = nonperiodic vector of the triple-cross-product term part A in one updating cycle δφ hB = nonperiodic vector of the triple-cross-product term part B in one updating cycle δφ hA = nonperiodic vector of the triple-cross-product term part A algorithms δφ hB = nonperiodic vector of the triple-cross-product term part B algorithms α N+1−i (t) = gyro data samples spaced backward in time from time t α N+1−j (t) α N+1−k (t) ς ij = coefficients of uncompressed frequency series coning algorithms and triple-cross-product term part A's algorithms η (N+i−1) = coefficients of triple-cross-product term part B's algorithms (N+j −1) (N+k−1) a, b = coning motion amplitude c = coefficient of constant angular-rate precession = coning frequency T 0 = sampling period T = attitude updating cycle N = total number of samples used in one attitude updating cycle 1178 IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS VOL. 51, NO. 2 APRIL 2015 n = number of samples in the current iteration time interval m = computer interval index, where the subscript indicates parameter value at computer cycle m o () = a...

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“…For this purpose, different bias and ramp type faults have been included in this section. Note that most faults which occur in the GPS are of bias or ramp type (Joerger 2009;Bruggemann 2011). As shown in Figure 13 (a), a bias fault f equal to 50% of the maximum value that provided by the GPS in healthy condition, is introduced at time t = 5000s in all three components of GPS1.…”

Section: N U M E R I C a L S I M U L A T I O Nmentioning

confidence: 99%

GPS based onboard orbit determination system providing fault management features for a LEO satellite

et al. 2013

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This paper presents accurate orbit determination (OD) of the Iran University of Science and Technology Satellite (IUSTSAT) from Global Positioning System (GPS) data. The GPS position data are treated as pseudo-measurements within an onboard orbit determination process that is based on the numerical integration of the equations of motion using an earth gravity model and applying an Extended Kalman Filter for the data processing. In this paper, through accurate tuning of GPS duty cycle and on/off time intervals, a solution is suggested to achieve the desired OD accuracy despite power constraints. Moreover, a new scheme for automatic fault management in the orbit determination system is derived that provides fault detection and accommodation features. K E Y WO R D S 1. Orbit determination. 2. GPS. 3. Kalman filter. 4. Fault detection and accommodation.

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GPS Fault Detection with IMU and Aircraft Dynamics

Cited by 33 publications

References 9 publications

Unified Model Technique for Inertial Navigation Aided by Vehicle Dynamics Model

Unified Model Technique for Inertial Navigation Aided by Vehicle Dynamics Model

High-order attitude compensation in coning and rotation coexisting environment

GPS based onboard orbit determination system providing fault management features for a LEO satellite

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