Novel Kalman filtering method for the suppression of gyroscope noise effects in pointing and tracking systems

Algrain, Marcelo C.; Ehlers, Douglas E.

doi:10.1117/12.210754

Cited by 15 publications

(7 citation statements)

References 4 publications

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“…The BSEKF solves the nonlinear least-squares problem of Eqs. (28) and 29using a guarded Gauss-Newton method, 90 which works with guesses and increments of…”

Section: E Backwards-smoothing Ekfmentioning

confidence: 99%

“…Interlaced filters that replace a nonlinear filter with two or more linear filters have been used for rate estimation, but not for attitude estimation. [27][28][29] Several new alternatives to the standard EKF have been recently introduced, such as sigma point or unscented filters [30][31][32][33] and particle filters. [34][35][36][37] Unscented filters (UFs) have been shown to exhibit several advantages over the EKF, including: 1) the expected error is lower than the EKF, 2) they can be applied to non-differentiable functions, 3) no Jacobian matrix calculations are required, and 4) they provide higherorder expansions than the standard EKF.…”

Section: Introductionmentioning

confidence: 99%

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Nonlinear Attitude Filtering Methods

Markley

Crassidis

Cheng

2005

AIAA Guidance, Navigation, and Control Conference and Exhibit

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This paper provides a survey of modern nonlinear filtering methods for attitude estimation. Early applications relied mostly on the extended Kalman filter for attitude estimation. Since these applications, several new approaches have been developed that have proven to be superior to the extended Kalman filter. Several of these approaches maintain the basic structure of the extended Kalman filter, but employ various modifications in order to provide better convergence or improve other performance characteristics. Examples of such approaches include: filter QUEST, extended QUEST, the super-iterated extended Kalman filter, the interlaced extended Kalman filter, and the second-order Kalman filter. Filters that propagate and update a discrete set of sigma points rather than using linearized equations for the mean and covariance are also reviewed. A two-step approach is discussed with a first-step state that linearizes the measurement model and an iterative second step to recover the desired attitude states. These approaches are all based on the Gaussian assumption that the probability density function is adequately specified by its mean and covariance. Other approaches that do not require this assumption are reviewed, including particle filters and a Bayesian filter based on a non-Gaussian, finite-parameter probability density function on S O ( 3 ) . Finally, the predictive filter, nonlinear observers and adaptive approaches are shown. The strengths and weaknesses of the various approaches are discussed.

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“…The BSEKF solves the nonlinear least-squares problem of Eqs. (28) and 29using a guarded Gauss-Newton method, 90 which works with guesses and increments of…”

Section: E Backwards-smoothing Ekfmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nonlinear Attitude Filtering Methods

Markley

Crassidis

Cheng

2005

AIAA Guidance, Navigation, and Control Conference and Exhibit

View full text Add to dashboard Cite

show abstract

“…Data fusion of the integrated system has been carried out for many years using Kalman filtering (KF) (Hostetler and Andreas, 1983;Hargrave, 1989), which utilizes a dynamic model of INS position, velocity and attitude errors as well as stochastic model of sensor errors. In fact, several shortcomings related to KF-based INS/GPS have been reported in the literature (Hargrave, 1989;Scherzinger and Reid, 1994;Algrain and Ehlers, 1995;Nassar et al, 2004;Brown and Hwang, 1992). The major inadequacy related to the utilization of KF for INS/GPS integration is the necessity to have a predefined accurate stochastic model for each of the sensor errors (Scherzinger and Reid, 1994;Algrain and Ehlers, 1995).…”

Section: Introductionmentioning

confidence: 96%

“…In fact, several shortcomings related to KF-based INS/GPS have been reported in the literature (Hargrave, 1989;Scherzinger and Reid, 1994;Algrain and Ehlers, 1995;Nassar et al, 2004;Brown and Hwang, 1992). The major inadequacy related to the utilization of KF for INS/GPS integration is the necessity to have a predefined accurate stochastic model for each of the sensor errors (Scherzinger and Reid, 1994;Algrain and Ehlers, 1995). Furthermore, prior information about the covariance values of both INS and GPS data as well as the statistical properties (i.e.…”

Section: Introductionmentioning

confidence: 96%

Optimizing neuro-fuzzy modules for data fusion of vehicular navigation systems using temporal cross-validation

Noureldin

El-Shafie

Taha

2007

Engineering Applications of Artificial Intelligence

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“…However, to date, most researches in this field have been concentrated on the friction torque [3,4] and gyro noises [5]. And very few papers have addressed the issue of developing methods to analyze and compensate the mass imbalance torque alone.…”

Section: Introductionmentioning

confidence: 99%

Acceleration-based mass imbalance feedforward compensation for inertial stabilized platform

Bai

Zhang

2014

Int. J. Control Autom. Syst.

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Focusing on the disturbance rejection for Inertial Stabilized Platform (ISP), an accelerationbased feedforward compensation method is proposed. The model of mass imbalance is established, through analyzing the relationships among the mass imbalance, the friction, the current of motor, and the attitude of gimbal. Then, the feedforward compensation controller is built for ISP using the data of accelerations acting on the ISP. It could estimate and compensate the mass imbalance torque of ISP in real time. The numerical simulations and vehicle experiments demonstrate the effectiveness of the designed controller. Compared to the traditional Proportion Integration Differentiation (PID) controller without feedforward compensation, the performance of ISP is significantly improved by the proposed compensation method in the presence of similar disturbances.

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Novel Kalman filtering method for the suppression of gyroscope noise effects in pointing and tracking systems

Cited by 15 publications

References 4 publications

Nonlinear Attitude Filtering Methods

Nonlinear Attitude Filtering Methods

Optimizing neuro-fuzzy modules for data fusion of vehicular navigation systems using temporal cross-validation

Acceleration-based mass imbalance feedforward compensation for inertial stabilized platform

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