An improved adaptive Sage filter with applications in GEO orbit determination and GPS kinematic positioning

Xu, Tianhe; Jiang, Nan; Sun, Zhangzhen

doi:10.1007/s11433-012-4659-z

Cited by 20 publications

(12 citation statements)

References 12 publications

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“…Consider the kinematic model function is equation 1and the observational model function is equation (2). Assume that kinematic model error and observational model error of the kalman filtering obey the zero mean normal distribution [7] [8].…”

Section: Model Systematic Errorsmentioning

confidence: 99%

“…Xu and Jiang presented an improved adaptive Sage filtering for kinematic positioning. It is a method to deal with observation and model noises by adaptively adjusting the covariance matrix of system state noise through the adaptive factor [2]. However, this method assumes that the prior covariance matrix of the kinematic model noise is the smoothing value of the model error within a moving time window [4]- [6].…”

Section: Introductionmentioning

confidence: 99%

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Research on Algorithm of Sage Filtering Considering the Model Systematic Errors

Gao¹,

Gao²

2017

IJET

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These This paper presents a new method for Sage filtering considering the model systematic errors. This method adopts Sage filtering established to estimate the model systematic error as well as the covariance matrices of observation residual vector, predicted residual vector, and predicted state vector within a moving time window. Experiment results and comparison analysis with the existing methods demonstrate that the proposed method of Sage filtering considering the model systematic errors can effectively resist the disturbances of the model error. The achieved navigation accuracy is much higher than the kalman filtering and Sage filtering methods.

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Section: Model Systematic Errorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on Algorithm of Sage Filtering Considering the Model Systematic Errors

Gao¹,

Gao²

2017

IJET

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“…The robust adaptive filtering is a method to deal with observation and model noises by robustly estimating the covariance matrix of observation noise and adaptively adjusting the covariance matrix of system state noise through the adaptive factor [19][20][21][22][23]. As one of the earliest works, Yang et al reported a robust adaptive filter by combining the robust maximum likelihood estimation with the adaptive filtering process to adaptively adjust the weight matrix of predicted parameters according to the difference between system observation and model information [19].…”

Section: Related Workmentioning

confidence: 99%

Sage windowing and random weighting adaptive filtering method for kinematic model error

Gao

Wei

Zhong

et al. 2015

IEEE Trans. Aerosp. Electron. Syst.

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This paper presents a new method for adaptive estimation of kinematic model error in dynamic aircraft navigation. This method combines the concepts of random weighting and Sage windowing to online monitor predicted and observation residuals to control the influence of the kinematic model's systematic error on system state estimation. Based on the Sage windowing, random weighting estimations are constructed within a moving time window for the systematic error of the kinematic model as well as the covariance matrices of the observation noise vector, the predicted residual vector, and the predicted state vector. Experimental results and comparison analysis demonstrate that the proposed method not only adjusts the covariance matrices of the observation noise vector and the predicted residual vector, but also effectively controls the influence of the kinematic model error on state parameter estimation, thus improving the navigation accuracy.Yongmin Zhong is a senior lecturer within the School of Aerospace, Mechanical and Manufacturing Engineering, RMIT University, Australia. His research interests include virtual reality and haptics, computational modelling, soft tissue modelling and surgery simulation, robotics, mechatronics, optimum estimation and control, and integrated navigation system.

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“…Furthermore, the orbit can be recovered quickly by the short method after maneuver . Research related to POD of maneuvered satellites has been already conducted by several authors (Beutler et al, 1994;Xu et al, 2012), e.g. the Kalman filter based POD method with http://dx.doi.org/10.1016/j.asr.2014.07.012 0273-1177/Ó 2014 COSPAR.…”

Section: Introductionmentioning

confidence: 99%

“…dynamic model compensation has been explained in (Xu et al, 2012). On the other hand, the explicit thrust model has not been discussed and the effect of utilizing observations before and after maneuver has not been analyzed in relevant literature.…”

Section: Introductionmentioning

confidence: 99%