scholarly journals An unscented Kalman filter method for real time input-parameter-state estimation

2022 ◽  
Vol 162 ◽  
pp. 108026
Author(s):  
Marios Impraimakis ◽  
Andrew W. Smyth
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Real Time ◽  
Unscented Kalman Filter ◽  
Input Parameter ◽  
Filter Method ◽  
Kalman Filter Method

scholarly journals Iterative Truncated Unscented Particle Filter

Information ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 214
Author(s):  
Yanbo Wang ◽  
Fasheng Wang ◽  
Jianjun He ◽  
Fuming Sun
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
State Estimation ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Process Models ◽  
Filter Method ◽  
Proposal Distribution ◽  
Truncation Method ◽  
Unscented Particle Filter

The particle filter method is a basic tool for inference on nonlinear partially observed Markov process models. Recently, it has been applied to solve constrained nonlinear filtering problems. Incorporating constraints could improve the state estimation performance compared to unconstrained state estimation. This paper introduces an iterative truncated unscented particle filter, which provides a state estimation method with inequality constraints. In this method, the proposal distribution is generated by an iterative unscented Kalman filter that is supplemented with a designed truncation method to satisfy the constraints. The detailed iterative unscented Kalman filter and truncation method is provided and incorporated into the particle filter framework. Experimental results show that the proposed algorithm is superior to other similar algorithms.

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 Real-Time Noninvasive Intracranial State Estimation Using Unscented Kalman Filter

2019 ◽  
Vol 27 (9) ◽  
pp. 1931-1938 ◽  
Author(s):  
Chanki Park ◽  
Seung Jun Ryu ◽  
Bong Hyun Jeong ◽  
Sang Pyung Lee ◽  
Chang-Ki Hong ◽  
...  
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Real Time ◽  
Unscented Kalman Filter

scholarly journals A Progressive Extended Kalman Filter Method for Freeway Traffic State Estimation Integrating Multisource Data

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Yingshun Liu ◽  
Shanglu He ◽  
Bin Ran ◽  
Yang Cheng
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Extended Kalman Filter ◽  
State Space Model ◽  
Heterogeneous Data ◽  
Filter Method ◽  
Multiple Sources ◽  
Freeway Traffic ◽  
Traffic Conditions ◽  
Kalman Filter Method

Variable techniques have been used to collect traffic data and estimate traffic conditions. In most cases, more than one technology is available. A legitimate need for research and application is how to use the heterogeneous data from multiple sources and provide reliable and consistent results. This paper aims to integrate the traffic features extracted from the wireless communication records and the measurements from the microwave sensors for the state estimation. A state-space model and a Progressive Extended Kalman Filter (PEKF) method are proposed. The results from the field test exhibit that the proposed method efficiently fuses the heterogeneous multisource data and adaptively tracks the variation of traffic conditions. The proposed method is satisfactory and promising for future development and implementation.

Real-time forecasting of fire in a two-story building using ensemble Kalman filter method

2018 ◽  
Vol 97 ◽  
pp. 19-28 ◽  
Author(s):  
Jie Ji ◽  
Chunxiang Liu ◽  
Zihe Gao ◽  
Liangzhu (Leon) Wang
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Ensemble Kalman Filter ◽  
Filter Method ◽  
Kalman Filter Method ◽  
Story Building
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