Instantaneous spectrum estimation of earthquake ground motions based on unscented Kalman filter method

2007 ◽  
Vol 28 (11) ◽  
pp. 1535-1543 ◽  
Author(s):  
Ying-min Li ◽  
Yin-feng Dong ◽  
Ming Lai
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Ground Motions ◽  
Filter Method ◽  
Spectrum Estimation ◽  
Instantaneous Spectrum ◽  
Earthquake Ground Motions ◽  
Kalman Filter Method

scholarly journals Estimation of Full Strap-down Rotating Bomb Guidance Information Based on Unscented Kalman Filter

2020 ◽  
Vol 38 (3) ◽  
pp. 604-609
Author(s):  
Wei Gao ◽  
Benbing Gao ◽  
Hongsong Fang ◽  
Xin Lu
Keyword(s):  
Mathematical Model ◽  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Angular Rate ◽  
The Body ◽  
Filter Method ◽  
Measurement Information ◽  
Kalman Filter Method ◽  
The Mathematical Model ◽  
Navigation Information

In this paper, the full strap-down seeker of rotating bomb is taken as the research object, and the method of extracting the LOS (line-of-sight) angle and angular rate of the full strap-down seeker of the rotating bomb is studied. The structure of the full strap-down seeker is quite different from that of the conventional rate gyro seeker. The measurement system of full strap-down seeker is fixed to the missile, the seeker can only obtain the measurement information in the projectile coordinate system, and the measurement information is coupled with the body posture information, so it cannot be directly used for the control guidance of the rotating projectile. First, based on the conversion relationship between coordinate systems, the mathematical model of the inertial LOS angle of the rotating bomb is established, and the mathematical model of the extraction of the inertial LOS angle and angular rate of the rotating bomb is further established. Then, the Kalman filter is designed by using the unscented Kalman filter method (UKF), and the extracted LOS angle containing noise information is filtered. Finally, the mathematical simulation is carried out to verify the validity of the mathematical model of LOS angle and angular rate extraction. Compared with the Extended Kalman filter method (EKF), the UKF has a higher accuracy for estimating the navigation information of the full strap-down rotating projectile.

scholarly journals General Model and SOC Estimation of Battery

2021 ◽  
Vol 15 ◽  
pp. 155-163
Author(s):  
Li Meng ◽  
Haipeng Guo ◽  
Xiaowei Zhao
Keyword(s):  
Kalman Filter ◽  
General Model ◽  
Unscented Kalman Filter ◽  
Measurement Data ◽  
Work Conditions ◽  
Filter Method ◽  
Soc Estimation ◽  
The Difference ◽  
Kalman Filter Method ◽  
Set Up

Monitoring the battery state is of great importance for the safety and normal of the systems which are powered by batteries. SOC (State of Charge) is one of the most important state parameters of battery. SOC cannot be measured directly. The Kalman filter algorithm is one of the techniques often applied to estimate SOC value. An accurate model is necessary for this algorithm. In this paper, a general SOC model is set up. It takes into account not only the difference between discharging and charging work conditions, but also the influence of the working atmosphere, such as temperature and discharging rate. Then based on this general model, unscented Kalman filter method is used to predict the SOC value. It can avoid the error which is caused by ignoring high-order terms, which is a shortcoming exist in the extended Kalman filter method. The simulation experiments prove the approach can get satisfactory results even when the measurement data is mixed with noise or the initial SOC value is not accurate.

scholarly journals Atmospheric PM2.5 concentration prediction and noise estimation based on adaptive unscented Kalman filtering

2021 ◽  
pp. 002029402199749
Author(s):  
Jihan Li ◽  
Xiaoli Li ◽  
Kang Wang ◽  
Guimei Cui
Keyword(s):  
Kalman Filter ◽  
Support Vector Regression ◽  
Unscented Kalman Filter ◽  
Atmospheric Environment ◽  
Filter Method ◽  
Support Vector ◽  
Adaptive Unscented Kalman Filter ◽  
Simulation Results ◽  
Kalman Filter Method ◽  
Concentration Prediction

Due to the randomness and uncertainty in the atmospheric environment, and accompanied by a variety of unknown noise. Accurate prediction of PM2.5 concentration is very important for people to prevent injury effectively. In order to predict PM2.5 concentration more accurately in this environment, a hybrid modelling method of support vector regression and adaptive unscented Kalman filter (SVR-AUKF) is proposed to predict atmospheric PM2.5 concentration in the case of incorrect or unknown noise. Firstly, the PM2.5 concentration prediction model was established by support vector regression. Secondly, the state space framework of the model is combined with the adaptive unscented Kalman filter method to estimate the uncertain PM2.5 concentration state and noise through continuous updating when the model noise is incorrect or unknown. Finally, the proposed method is compared with SVR-UKF method, the simulation results show that the proposed method is more accurate and robust. The proposed method is compared with SVR-UKF, AR-Kalman, AR and BP methods. The simulation results show that the proposed method has higher prediction accuracy of PM2.5 concentration.

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