Adaptive Kalman Filter Based Aircraft Ground Icing Thickness Prediction

2012 ◽  
Vol 562-564 ◽  
pp. 1660-1667
Author(s):  
Zhi Wei Xing ◽  
Hui Zhang ◽  
Zhun Ren
Keyword(s):  
Kalman Filter ◽  
Prediction Model ◽  
Prediction Method ◽  
Theory And Practice ◽  
Filter Method ◽  
Support Vector ◽  
Aircraft Icing ◽  
Adaptive Kalman Filter ◽  
Thickness Prediction ◽  
Kalman Filter Method

The nonlinear dynamics model is used to describe the change of aircraft icing thickness and icing deformation accelerations is viewed as dynamic noise in this paper. Then, a dynamic prediction model of aircraft icing thickness is established with the theory of adaptive kalman filter. And the adaptive kalman filter method based aircraft icing thickness prediction model is employed to forecast aircraft ground icing thickness and compared with support vector machine, BP neural network prediction method. The result of the instance simulation and analysis indicates that the adaptive kalman filter method based aircraft icing thickness prediction posed in this paper is reliable, simple and rapid, and the model has high prediction precision which can realize real-time tracking and prediction and has definite value of both theory and practice.

scholarly journals State of Charge Estimation of a Lithium Ion Battery Based on Adaptive Kalman Filter Method for an Equivalent Circuit Model

2019 ◽  
Vol 9 (13) ◽  
pp. 2765 ◽  
Author(s):  
Xiao Ma ◽  
Danfeng Qiu ◽  
Qing Tao ◽  
Daiyin Zhu
Keyword(s):  
Kalman Filter ◽  
Equivalent Circuit ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Circuit Model ◽  
State Of Charge ◽  
Filter Method ◽  
Adaptive Kalman Filter ◽  
Soc Estimation ◽  
Kalman Filter Method

Due to its accuracy, simplicity, and other advantages, the Kalman filter method is one of the common algorithms to estimate the state-of-charge (SOC) of batteries. However, this method still has its shortcomings. The Kalman filter method is an algorithm designed for linear systems and requires precise mathematical models. Lithium-ion batteries are not linear systems, so the establishment of the battery equivalent circuit model (ECM) is necessary for SOC estimation. In this paper, an adaptive Kalman filter method and the battery Thevenin equivalent circuit are combined to estimate the SOC of an electric vehicle power battery dynamically. Firstly, the equivalent circuit model is studied, and the battery model suitable for SOC estimation is established. Then, the parameters of the corresponding battery charge and the discharge experimental detection model are designed. Finally, the adaptive Kalman filter method is applied to the model in the unknown interference noise environment and is also adopted to estimate the SOC of the battery online. The simulation results show that the proposed method can correct the SOC estimation error caused by the model error in real time. The estimation accuracy of the proposed method is higher than that of the Kalman filter method. The adaptive Kalman filter method also has a correction effect on the initial value error, which is suitable for online SOC estimation of power batteries. The experiment under the BBDST (Beijing Bus Dynamic Stress Test) working condition fully proves that the proposed SOC estimation algorithm can hold the satisfactory accuracy even in complex situations.

scholarly journals A reduction method for magnetic disturbance caused by DC railway system

2021 ◽  
Vol 73 (1) ◽  
Author(s):  
Xuezhen Ding ◽  
Yuguo Li ◽  
Yunju Wu ◽  
Shuangmin Duan ◽  
Zhuoxuan Li ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Reduction Method ◽  
Filter Method ◽  
Magnetic Disturbance ◽  
Adaptive Kalman Filter ◽  
Railway Systems ◽  
Geomagnetic Data ◽  
Railway System ◽  
Kalman Filter Method ◽  
High Level

AbstractThe stray current of direct current (DC) railway systems causes magnetic disturbance in geomagnetic measurements, which may complicate the identification of useful information. The magnetic disturbance exhibits broadband characteristics in the frequency domain. In this paper, we propose a noise reduction method based on the adaptive Kalman filter to extract useful signals from the geomagnetic data with a high level of noise. The covariance matrixes of both the process noise (Q) and measurement noise (R) can be adaptively estimated to improve the performance of the adaptive Kalman filter. The proposed method is adopted to process the geomagnetic data collected at the Beijing Geomagnetic Observatory (BJI), which is affected by the DC railway system. The magnetic disturbance is largely reduced, and the signal-to-noise ratios of the horizontal and vertical components of the geomagnetic field are improved by more than 14 dB and 27 dB, respectively. The K-indices are calculated to evaluate the performance of the adaptive Kalman filter method. To assess the influence of the adaptive Kalman filter on natural signals, the geomagnetic data that contain rapid variations are processed. The denoising results show that the adaptive Kalman filter can effectively reduce the magnetic disturbance caused by DC railway system without large impact on the natural geomagnetic rapid variations.

Estimation of SOC Based on Subspace Model of Power Battery and Adaptive Kalman Filter Method

2013 ◽  
Vol 448-453 ◽  
pp. 1423-1427
Author(s):  
Min Xin Zheng ◽  
Qing Sen Yang
Keyword(s):  
Kalman Filter ◽  
State Space ◽  
State Space Model ◽  
Equivalent Circuit Model ◽  
Filter Method ◽  
Adaptive Kalman Filter ◽  
Space Model ◽  
Kalman Filter Method ◽  
Set Up ◽  
The Relationship

Subspace identification method was adopted to build a state-space model of the battery pack by directly using the acquisition data of current and voltage. The terminal voltage was split into four parts according to the relationship between the current and each element of the models output voltage. Then an equivalent circuit model composed of resistances and capacities was set up to simulate the relationship. Based on the battery model, a state space model with SOC as the state variable and voltage UCb as the output was set up. By applying a designed adaptive Kalman filter method to the model and adopting the voltage UT1 from the subspace method as the measured output, the optimum estimation of SOC can be acquired with only calculations of one dimension.

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.

scholarly journals Performance evaluation of GPS / BDS combined single-point positioning based on RTKLIB

2020 ◽  
Vol 165 ◽  
pp. 03009
Author(s):  
Li Yan-yi ◽  
Huang Jin ◽  
Tang Ming-xiu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Single Point ◽  
Least Square Method ◽  
Least Square ◽  
Filter Method ◽  
Single System ◽  
Positioning Error ◽  
Dual Mode ◽  
Kalman Filter Method

In order to evaluate the performance of GPS / BDS, RTKLIB, an open-source software of GNSS, is used in this paper. In this paper, the least square method, the weighted least square method and the extended Kalman filter method are respectively applied to BDS / GPS single system for data solution. Then, the BDS system and GPS system are used for fusion positioning and the positioning results of the two systems are compared with that of the single system. Through the comparison of experiments, on the premise of using the extended Kalman filter method for positioning, when the GPS signal is not good, BDS data is introduced for dual-mode positioning, the positioning error in e direction is reduced by 36.97%, the positioning error in U direction is reduced by 22.95%, and the spatial positioning error is reduced by 16.01%, which further reflects the advantages of dual-mode positioning in improving a system robustness and reducing the error.

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