Attitude Estimation Based on the Spherical Simplex Transformation Modified Unscented Kalman Filter

Zhao, Jianwei; Jia, Weimin; Wang, Rong; Yan, Zhixiang

doi:10.1155/2014/925914

Cited by 6 publications

(2 citation statements)

References 14 publications

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“…Step tracking and conical scan are two examples of tracking algorithms described in this article, which use only the radio signal strength measurements to find the optimal orientation of the antenna. Previous attempts to improve these algorithms were made, but they only used measurements from the inertial unit [2] or the actual signal strength [3], not both pieces of information together.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Maximum Signal Strength for Satellite Tracking Based on the Extended Kalman Filter

Bastl

Spáčil

Najman

et al. 2023

AiMT

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The article presents an improved satellite tracking approach using the extended Kalman filter within the control systems of the moving antenna. The main focus is on the issue of maintaining the maximum signal strength during vehicle movement, gyroscope and GPS sensors error and the deterioration of reception due to changing conditions (weather, obstacles). Typically used techniques are based on ad-hoc scanning of the maximum value of the RF signal. The approach presented in this article might be used as a much more consistent and elegant alternative to these algorithms. To prove the function of the presented algorithm, simulations of several different scenarios are performed. Based on these simulations, the robustness and speed of the newly designed algorithm are evaluated.

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Section: Introductionmentioning

confidence: 99%

Estimation of Maximum Signal Strength for Satellite Tracking Based on the Extended Kalman Filter

Bastl

Spáčil

Najman

et al. 2023

AiMT

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show abstract

“…However, with the change of vehicle motion state, the measurement noise of the heading angle variation reflects different noise characteristics. If the standard UKF algorithm is still used at this time, then the measurement noise covariance matrix set in the initial filtering cannot accurately describe the measurement noise in real-time, which will be resulting in unstable filtering [18].…”

Section: Introductionmentioning

confidence: 99%

A Heading Angle Estimation Approach for MEMS-INS/GNSS Integration Based on ZHVC and SAUKF

et al. 2019

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In the traditional MEMS-INS/GNSS integration, velocity and position are regarded as measurement information. However, it will lead to unobservable of the heading angle, and the heading angle error will be divergent. Eventually, the navigation accuracy will be reduced either. In this paper, a novel approach of heading angle estimation based on Zero-Heading angle-Variation-Constraint (ZHVC) and Sequential-Adaptive Unscented Kalman Filter (SAUKF) algorithms is proposed for avoiding the heading angle unobservability and unstable of the filtering. First, we inspired by the vehicle dynamics and add new information to the original measurement vectors. This new measurement information is the difference of heading angle variations from the MEMS-INS and the theoretical value. Second, we separate the measurement update of Unscented Kalman Filter (UKF) to two parts by sequential method. One is the measurement information of velocity and position, the other one is the heading angle variation. Meanwhile, adaptive UKF only estimate the heading angle variation measurement noise covariance matrix in real-time. The simulation and experiment show that ZHVC can improve the observable degree and accuracy of heading angle than the common method. The SAUKF can estimate heading angle variation measurement noise covariance matrix in real-time. And the filter results are more stable in different motions of the vehicle.INDEX TERMS ZHVC, SAUKF, observable degree, measurement noise covariance matrix.

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Model Based Fault Diagnosis of Low Earth Orbiting (LEO) Satellite using Spherical Unscented Kalman Filter

et al. 2016

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Attitude Estimation Based on the Spherical Simplex Transformation Modified Unscented Kalman Filter

Cited by 6 publications

References 14 publications

Estimation of Maximum Signal Strength for Satellite Tracking Based on the Extended Kalman Filter

Estimation of Maximum Signal Strength for Satellite Tracking Based on the Extended Kalman Filter

A Heading Angle Estimation Approach for MEMS-INS/GNSS Integration Based on ZHVC and SAUKF

Model Based Fault Diagnosis of Low Earth Orbiting (LEO) Satellite using Spherical Unscented Kalman Filter

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