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RISS 인기검색어
- 검색키워드
- 저자 : Chengfan Gu (검색결과 3 건)
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Random Weighting Estimation for Systematic Error of Observation Model in Dynamic Vehicle Navigation
Wenhui Wei,Shesheng Gao,Yongmin Zhong,Chengfan Gu,Aleksandar Subic 제어·로봇·시스템학회 2016 International Journal of Control, Automation, and Vol.14 No.2
The Kalman filter requires kinematic and observation models not contain any systematic error. Otherwise,the resultant navigation solution will be biased or even divergent. In order to overcome this limitation, thispaper presents a new random weighting method to estimate the systematic error of observation model in dynamicvehicle navigation. This method randomly weights the covariance matrices of observation residual vector, predictedresidual vector and estimated state vector to control their magnitudes, thus governing the random weighting estimationfor the covariance matrix of observation vector. Random weighting theories are established for estimationsof the observation model’s systematic error and the covariance matrices of observation residual vector, predictedresidual vector, observation vector and estimated state vector. Experiments and comparison analysis with the existingmethods demonstrate that the proposed random weighting method can effectively resist the disturbance ofthe observation model’s systematic error on the state parameter estimation, leading to the improved accuracy fordynamic vehicle navigation.
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Interacting Multiple Model Estimation-based Adaptive Robust Unscented Kalman Filter
Bingbing Gao,Shesheng Gao,Yongmin Zhong,Gaoge Hu,Chengfan Gu 제어·로봇·시스템학회 2017 International Journal of Control, Automation, and Vol.15 No.5
The unscented Kalman filter (UKF) is a promising approach for the state estimation of nonlinear dynamicsystems due to its simple calculation process and superior performance in highly nonlinear systems. However, itssolution will be degraded or even divergent when the system model involves uncertainty. This paper presents aninteracting multiple model (IMM) estimation-based adaptive robust UKF to address this problem. This methodcombines the merits of the adaptive fading UKF and robust UKF and discards their demerits to inhibit the disturbanceof system model uncertainty on the filtering solution. An adaptive fading UKF for the case of process modeluncertainty and a robust UKF for the case of measurement model uncertainty are established based on the principleof innovation orthogonality. Subsequently, an IMM estimation is developed to fuse the adaptive fading UKF androbust UKF as sub-filters according to the mode probability. The system state estimation is achieved as a probabilisticweighted sum of the estimation results from the two sub-filters. Simulations, experiments and comparisonanalysis validate the efficacy of the proposed method.
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Multi-sensor Optimal Data Fusion for INS/GNSS/CNS Integration Based on Unscented Kalman Filter
Bingbing Gao,Gaoge Hu,Shesheng Gao,Yongmin Zhong,Chengfan Gu 제어·로봇·시스템학회 2018 International Journal of Control, Automation, and Vol.16 No.1
This paper presents an unscented Kalman filter (UKF) based multi-sensor optimal data fusion methodology for INS/GNSS/CNS (inertial navigation system/global navigation satellite system/celestial navigation system) integration based on nonlinear system model. This methodology is of two-level structure: at the bottom level, the UKF is served as local filters to integrate GNSS and CNS with INS respectively for generating the local optimal state estimates; and at the top level, a novel optimal data fusion approach is derived based on the principle of linear minimum variance for the fusion of local state estimates to obtain the global optimal state estimation. The proposed methodology refrains from the use of covariance upper bound to eliminate the correlation between local states. Its efficacy is verified through simulations, practical experiments and comparison analysis with the existing methods for INS/GNSS/CNS integration.
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