Coal mine personnel positioning algorithm based on improved adaptive unscented Kalman filter with wireless channel fading and unknown noise statistics

2021 ◽  
pp. 014233122110512
Author(s):  
Xingzhen Bai ◽  
Hongxiang Xu ◽  
Jing Li ◽  
Xuehui Gao ◽  
Feiyu Qin ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Coal Mine ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Wireless Channel ◽  
Energy Utilization ◽  
Channel Fading ◽  
Adaptive Unscented Kalman Filter ◽  
Save Energy ◽  
Noise Statistics

This paper is concerned with the problem of personnel localization in the complex coal mine environment with wireless channel fading and unknown noise statistics. Considering the random channel fading caused by signal fluctuation and transmission fault, an improved adaptive unscented Kalman filter (IAUKF) algorithm is proposed. The mean and error covariances of noise are estimated adaptively by adopting the improved Sage–Husa noise estimation method. In order to save energy and improve energy utilization, the multi-sensor clustering is performed to divide the spatial distribution of sensors into multiple clusters. The sensors in the same cluster can communicate with each other to maintain the consistency of estimation. The simulation results show that the IAUKF algorithm is better than extended Kalman filter (EKF), unscented Kalman filter (UKF), and improved unscented Kalman filter (IUKF) algorithms.

scholarly journals State of Charge Estimation of Lithium-Ion Battery Based on Improved Adaptive Unscented Kalman Filter

2021 ◽  
Vol 13 (9) ◽  
pp. 5046
Author(s):  
Jie Xing ◽  
Peng Wu
Keyword(s):  
Kalman Filter ◽  
Lithium Ion Battery ◽  
Unscented Kalman Filter ◽  
Lithium Ion ◽  
State Of Charge ◽  
Estimation Accuracy ◽  
Soc Estimation ◽  
Error Covariance ◽  
Adaptive Unscented Kalman Filter ◽  
Noise Statistics

State of charge (SOC) of the lithium-ion battery is an important parameter of the battery management system (BMS), which plays an important role in the safe operation of electric vehicles. When existing unknown or inaccurate noise statistics of the system, the traditional unscented Kalman filter (UKF) may fail to estimate SOC due to the non-positive error covariance of the state vector, and the SOC estimation accuracy is not high. Therefore, an improved adaptive unscented Kalman filter (IAUKF) algorithm is proposed to solve this problem. The IAUKF is composed of the improved unscented Kalman filter (IUKF) that is able to suppress the non-positive definiteness of error covariance and Sage–Husa adaptive filter. The IAUKF can improve the SOC estimation stability and can improve the SOC estimation accuracy by estimating and correcting the system noise statistics adaptively. The IAUKF is verified under the federal urban driving schedule test, and the SOC estimation results are compared with IUKF and UKF. The experimental results show that the IAUKF has higher estimation accuracy and stability, which verifies the effectiveness of the proposed method.

scholarly journals Vehicle Driving State Estimation Using an Improved Adaptive Unscented Kalman Filter

2021 ◽  
Author(s):  
Hui Pang ◽  
Peng Wang ◽  
Zijun Xu ◽  
Gang Wang
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Estimation Errors ◽  
Adaptive Unscented Kalman Filter ◽  
Adjustment Strategy ◽  
Noise Covariance ◽  
Adaptive Noise ◽  
Three Degree Of Freedom

Abstract This paper proposes an improved adaptive unscented Kalman filter (iAUKF)-based vehicle driving state estimation method. A three-degree-of-freedom vehicle dynamics model is first established, then the varying principles of estimation errors for vehicle driving states using constant process and measurement noises in the standard unscented Kalman filter (UKF) are compared and analyzed. Next, a new type of normalized innovation square-based adaptive noise covariance adjustment strategy is designed and incorporated into the UKF to derive our expected vehicle driving state estimation method. Finally, a comparative simulation investigation using CarSim and MATLAB/Simulink is conducted to validate the effectiveness of the proposed method, and the results show that our proposed iAUKF-based estimation method has higher accuracy and stronger robustness against the standard UKF algorithm.

scholarly journals Atmospheric PM2.5 Prediction Based on Multiple Model Adaptive Unscented Kalman Filter

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 607
Author(s):  
Jihan Li ◽  
Xiaoli Li ◽  
Kang Wang ◽  
Guimei Cui
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Support Vector ◽  
Multiple Model ◽  
Single Model ◽  
Adaptive Unscented Kalman Filter ◽  
Time Periods ◽  
Atmospheric Pm2.5 ◽  
Concentration Prediction ◽  
Pm2.5 Concentration

The PM2.5 concentration model is the key to predict PM2.5 concentration. During the prediction of atmospheric PM2.5 concentration based on prediction model, the prediction model of PM2.5 concentration cannot be usually accurately described. For the PM2.5 concentration model in the same period, the dynamic characteristics of the model will change under the influence of many factors. Similarly, for different time periods, the corresponding models of PM2.5 concentration may be different, and the single model cannot play the corresponding ability to predict PM2.5 concentration. The single model leads to the decline of prediction accuracy. To improve the accuracy of PM2.5 concentration prediction in this solution, a multiple model adaptive unscented Kalman filter (MMAUKF) method is proposed in this paper. Firstly, the PM2.5 concentration data in three time periods of the day are taken as the research object, the nonlinear state space model frame of a support vector regression (SVR) method is established. Secondly, the frame of the SVR model in three time periods is combined with an adaptive unscented Kalman filter (AUKF) to predict PM2.5 concentration in the next hour, respectively. Then, the predicted value of three time periods is fused into the final predicted PM2.5 concentration by Bayesian weighting method. Finally, the proposed method is compared with the single support vector regression-adaptive unscented Kalman filter (SVR-AUKF), autoregressive model-Kalman (AR-Kalman), autoregressive model (AR) and back propagation neural network (BP). The prediction results show that the accuracy of PM2.5 concentration prediction is improved in whole time period.

Real-time SOC Estimation of the EKF and UKF Algorithms Under Different Working Conditions

2021 ◽  
pp. 1-29
Author(s):  
Xiongbin Peng ◽  
Yuwu Li ◽  
Wei Yang ◽  
Akhil Garg
Keyword(s):  
Kalman Filter ◽  
Working Conditions ◽  
Unscented Kalman Filter ◽  
Stress Test ◽  
Estimation Method ◽  
Influential Factor ◽  
Recursive Least Squares ◽  
Maximum Deviation ◽  
Soc Estimation ◽  
Proposed Model

Abstract In the battery thermal management system (BMS), the state of charge (SOC) is a very influential factor, which can prevent overcharge and over-discharge of the lithium-ion battery (LIB). This paper proposed a battery modeling and online battery parameter identification method based on the Thevenin equivalent circuit model (ECM) and recursive least squares (RLS) algorithm. The proposed model proved to have high accuracy. The error between the ECM terminal voltage value and the actual value basically fluctuates between ±0.1V. The extended Kalman filter (EKF) algorithm and the unscented Kalman filter (UKF) algorithm were applied to estimate the SOC of the battery based on the proposed model. The SOC experimental results obtained under dynamic stress test (DST), federal urban driving schedule (FUDS), and US06 cycle conditions were analyzed. The maximum deviation of the SOC based on EKF was 1.4112%~2.5988%, and the maximum deviation of the SOC based on UKF was 0.3172%~0.3388%. The SOC estimation method based on UKF and RLS provides a smaller deviation and better adaptability in different working conditions, which makes it more implementable in a real-world automobile application.

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