scholarly journals A Robust Adaptive Unscented Kalman Filter for Nonlinear Estimation with Uncertain Noise Covariance

Sensors ◽  
2018 ◽  
Vol 18 (3) ◽  
pp. 808 ◽  
Author(s):  
Binqi Zheng ◽  
Pengcheng Fu ◽  
Baoqing Li ◽  
Xiaobing Yuan
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Nonlinear Estimation ◽  
Adaptive Unscented Kalman Filter ◽  
Noise Covariance ◽  
Robust Adaptive

scholarly journals Quaternion-Based Robust Attitude Estimation Using an Adaptive Unscented Kalman Filter

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2372 ◽  
Author(s):  
Antônio C. B. Chiella ◽  
Bruno O. S. Teixeira ◽  
Guilherme A. S. Pereira
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Ground Truth ◽  
Adaptive Strategy ◽  
Attitude Estimation ◽  
Covariance Matching ◽  
Adaptive Unscented Kalman Filter ◽  
Unit Quaternions ◽  
Abrupt Changes ◽  
Robust Adaptive

This paper presents the Quaternion-based Robust Adaptive Unscented Kalman Filter (QRAUKF) for attitude estimation. The proposed methodology modifies and extends the standard UKF equations to consistently accommodate the non-Euclidean algebra of unit quaternions and to add robustness to fast and slow variations in the measurement uncertainty. To deal with slow time-varying perturbations in the sensors, an adaptive strategy based on covariance matching that tunes the measurement covariance matrix online is used. Additionally, an outlier detector algorithm is adopted to identify abrupt changes in the UKF innovation, thus rejecting fast perturbations. Adaptation and outlier detection make the proposed algorithm robust to fast and slow perturbations such as external magnetic field interference and linear accelerations. Comparative experimental results that use an industrial manipulator robot as ground truth suggest that our method overcomes a trusted commercial solution and other widely used open source algorithms found in the literature.

scholarly journals Estimation of the State of Charge of Lithium Batteries Based on Adaptive Unscented Kalman Filter Algorithm

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1425
Author(s):  
Jiechao Lv ◽  
Baochen Jiang ◽  
Xiaoli Wang ◽  
Yirong Liu ◽  
Yucheng Fu
Keyword(s):  
Kalman Filter ◽  
Electric Vehicle ◽  
Unscented Kalman Filter ◽  
The State ◽  
State Of Charge ◽  
Soc Estimation ◽  
System Noise ◽  
Adaptive Unscented Kalman Filter ◽  
Noise Covariance ◽  
Observation Noise

The state of charge (SOC) estimation of the battery is one of the important functions of the battery management system of the electric vehicle, and the accurate SOC estimation is of great significance to the safe operation of the electric vehicle and the service life of the battery. Among the existing SOC estimation methods, the unscented Kalman filter (UKF) algorithm is widely used for SOC estimation due to its lossless transformation and high estimation accuracy. However, the traditional UKF algorithm is greatly affected by system noise and observation noise during SOC estimation. Therefore, we took the lithium cobalt oxide battery as the analysis object, and designed an adaptive unscented Kalman filter (AUKF) algorithm based on innovation and residuals to estimate SOC. Firstly, the second-order RC equivalent circuit model was established according to the physical characteristics of the battery, and the least square method was used to identify the parameters of the model and verify the model accuracy. Then, the AUKF algorithm was used for SOC estimation; the AUKF algorithm monitors the changes of innovation and residual in the filter and updates system noise covariance and observation noise covariance in real time using innovation and residual, so as to adjust the gain of the filter and realize the optimal estimation. Finally came the error comparison analysis of the estimation results of the UKF algorithm and AUKF algorithm; the results prove that the accuracy of the AUKF algorithm is 2.6% better than that of UKF algorithm.

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.

Adaptive Unscented Kalman Filter for Tracking GPS Signals in the Case of an Unknown and Time-Varying Noise Covariance

2021 ◽  
Vol 29 (3) ◽  
pp. 34-51
Author(s):  
М.М. Kanouj ◽  
◽  
А.V. Klokov ◽  
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Tracking System ◽  
Doppler Frequency ◽  
Gps Tracking ◽  
Noisy Environments ◽  
Signal Tracking ◽  
Adaptive Unscented Kalman Filter ◽  
Noise Covariance ◽  
Conventional Algorithm

A new adaptive unscented Kalman filter (AUKF) is proposed to estimate the radio navigation parameters of a GPS signal tracking system in noisy environments and on a highly dynamic object. The experimental results have shown that the proposed AUKFbased method improves the GPS tracking margin by approximately 8 dB and 3 dB as compared to the conventional algorithm and the KF-based tracking, respectively. At the same time, the accuracy of Doppler frequency measurements increases as well.

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