Synthetically adaptive robust filtering for satellite orbit determination

Yang, Yuanxi; Wen, Yuanlan

doi:10.1360/02yd0211

Cited by 16 publications

(9 citation statements)

References 5 publications

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“…[1][2][3][4][5][6][7] Previous studies have proved that the SP film is biodegradable. [2][3][4][5][6][7][8][9] In the preparation of the SP film, formaldehyde is usually used as a crosslinking agent.…”

Section: Introductionmentioning

confidence: 99%

Starch crosslinked with poly(vinyl alcohol) by boric acid

Yin

Liu

et al. 2005

J of Applied Polymer Sci

124

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Starch was crosslinked with poly(vinyl alcohol) (PVA) by boric acid. A suitable plasticizer and defoamer were added to obtain the brei. A film from the starch and PVA (SP film) was prepared by casting. The effects of various factors, such as the crosslinking temperature, the PVA content, and the amounts of glycerol and boric acid, on the tensile strength and breaking elongation were studied. The results showed that the SP film prepared by boric acid crosslinking had excellent mechanical properties. The filmforming properties, transmittance, and water resistance of the SP film were also investigated.

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Section: Introductionmentioning

confidence: 99%

Starch crosslinked with poly(vinyl alcohol) by boric acid

Yin

Liu

et al. 2005

J of Applied Polymer Sci

124

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“…However, similar to the Sage windowing, this method estimates the covariance matrix of observation at the current epoch based on the equivalent contributions of all residuals from historical epochs, leading to the limited estimation accuracy. Yang et al reported an improved Sage adaptive filtering method [4,5]. This method obtains predicted residual vectors according to the smoothing solutions of the Sage windowing and further adjusts them by using an adaptive factor.…”

Section: Related Workmentioning

confidence: 99%

“…Both models exhibit statistical characteristics in terms of accuracy. The Kalman filter requires the observation error and kinematic model error of a system to be normally distributed with zero mean, that is, kinematic and observation models do not contain any systematic error [4][5][6]. However, in practice, kinematic and observation models always involve errors, due to the disturbances caused by abnormal observations and the uncertainties involved in dynamic aircraft navigation.…”

Section: Introductionmentioning

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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“…On the other hand, it is very difficult to predict the error distribution or the error type of the updated parameters and it is also difficult to construct the stochastic dynamic errors model. In this case, an adaptive robust filter is suitable to balance the dynamic model information and the measurements [19].…”

Section: Adaptive Robust Filtering For the Utc Systemmentioning

confidence: 99%

“…Furthermore, it is realised that the any errors of the dynamic model will be propagated to the estimates of the state parameters. So the proper estimation methods require that the estimation algorithms and the principle should balance the weight effect of using the measurements and the dynamic model information [19]. To rationally take advantage of the observational information and the dynamic model information to gain the accurate estimates of vehicle states, a novel adaptive robust UTC navigation system based on federated architecture and the developed GPS/ BeiDou vector tracking algorithms is presented.…”

Section: Introductionmentioning

confidence: 99%

Adaptive robust ultra‐tightly coupled global navigation satellite system/inertial navigation system based on global positioning system/BeiDou vector tracking loops

Xie

Liu

et al. 2014

IET Radar, Sonar & Navigation

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With the development of global navigation satellite system (GNSS), the GNSS/inertial navigation system (INS) integrated system offers the users better positioning or navigation performance. This paper proposes an adaptive robust ultratightly coupled GNSS/INS system based on a novel vector tracking strategy for combining both global positioning system (GPS) L1 and BeiDou B1 signals' tracking together. The inherent mechanism of the vector tracking approach has been analysed to describe the relationship between the replica signals and user's dynamic state. Then, an adaptive robust filter is used to gain the accurate estimates of vehicle states when the vehicle is under a weak-signal or large manoeuvring environment. Finally, the experimental platform is set up using a GPS/BeiDou signal simulator and an inertial measurement unit simulator and the test results show that the proposed ultra-tightly coupled system can keep the tracking loops from the high dynamic perturbations, which saves the cost time of signal reacquisition. Moreover, the presented adaptive robust ultratightly coupled system can obtain a higher accuracy than Kalman filtering in a simultaneous weak-signal and large manoeuvring environment.

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Synthetically adaptive robust filtering for satellite orbit determination

Cited by 16 publications

References 5 publications

Starch crosslinked with poly(vinyl alcohol) by boric acid

Starch crosslinked with poly(vinyl alcohol) by boric acid

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

Adaptive robust ultra‐tightly coupled global navigation satellite system/inertial navigation system based on global positioning system/BeiDou vector tracking loops

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