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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.
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.
( Eun Kyong Gaog ),( Joo Han Song ),( Young Seok Lee ),( Moo Suk Park ),( Young Sam Kim ),( Se Kyu Kim ),( Joon Chang ),( Kyung Soo Chung ) 대한결핵 및 호흡기학회 2015 대한결핵 및 호흡기학회 추계학술대회 초록집 Vol.120 No.-
Background: In critically ill patients, it is important to identify patients at high risk of death. We aimed to assess prognostic value of new score made by combination of delta neutrophil index (DNI) and thrombotic microangiopathic (TMA) score which are easily gained from complete blood cell counts. Methods: It is a retrospective pilot study. The patients who were admitted to MICU between June 2015 and July 2015 were studied. The laboratory data was collected within 24hrs from admission to ICU and the primary end-point is 28-day mortality. The TMA score is assigned a point value from 0 to 5. This is defined by the following 5 categories; Red cell distribution width>15%, Hemoglobin distribution width>3.2%, Microcytes>0.4%, Hyperchromic red cells>1.9%, Platelet count<140,000/㎕. The new score defined as DNI multiplied by TMA score. Results: A total of 86 patients were enrolled. There were 54males(62.8%) and 32females(37.2%). The patient’s median age was 68.6years (30-92years). The 28-day mortality rates were 30.2%(n=26). Non-survivors showed higher APACHEII, SOFA score, RDW, TMA, and new score. Receiver operating characteristic (ROC) analysis showed that area under the curve (AUC) of new score to predict 28-day mortality was 0.704(P=0.003). Each AUC was 0.706(P=0.003) in SOFA, 0.649(P=0.029) in APACHEII, 0.651(P=0.026) in TMA score, 0.631(P=0.054) in DNI, 0.595(P=0.069) in procalcitonin and 0.615(P=0.104) in CRP. There are no differences between ROC of new score and APACHEII or SOFA. Conclusions: The new score might reflect clinical outcomes in critically ill patients.