scholarly journals Improved Different Dimensional Sensors Combined Space Registration Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Yuming Bo ◽  
Zhimin Chen ◽  
Mingfeng Yin ◽  
Tianxiong Wang
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Attenuation Factor ◽  
Infrared Sensor ◽  
Infrared Sensors ◽  
Error Covariance ◽  
Covariance Matching ◽  
Federated Filtering ◽  
Matching Criteria ◽  
System Deviation

To address the problem of deviation and registration of 3D radar and infrared sensor, this paper presents and improves a method based on the state value and space deviation of federated filtering of unscented Kalman filter and standard Kalman filter, which conduces to real time registering of system deviation of radar and IF sensors. In the method presented here, a covariance matching criteria-based approach was employed for judgment of filtering divergent trend, while self-adaptive attenuation factor was introduced for correction of the predicted error covariance so as to inhibit the divergent phenomenon. The experiment results indicated that the method presented here conduces to improvement of the precision and speed of space registration, showing practical value in deviation registration of 3D radars and infrared sensors.

scholarly journals Adaptive fading factor unscented Kalman filter with application to target tracking

2020 ◽  
Author(s):  
Peng Gu ◽  
Zhongliang Jing ◽  
Liangbin Wu
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Unscented Kalman Filter ◽  
System Model ◽  
Mismatch Error ◽  
Tracking Accuracy ◽  
Model Mismatch ◽  
Error Covariance ◽  
Nonlinear Tracking ◽  
Fading Factor

AbstractOne purpose of target tracking is to estimate the states of targets, and unscented Kalman filter is one of the effective algorithms for estimating in the nonlinear tracking problem. Considering the characteristics of complex maneuverability, it is easy to reduce the tracking accuracy and cause divergence due to the mismatch between the system model and the practical target motion model. Adaptive fading factor is an effective counter to this problem, having been instrumental in solving accuracy and divergence problems. Fading factor can adaptively adjust covariance matrix online to compensate model mismatch error. Moreover, fading factor not only improves the filtering accuracy, but also automatically adjusts the error covariance in response to the different situation. The simulation results show that the adaptive fading factor unscented Kalman filter has more advantages in target tracking and it can be better applied to nonlinear target tracking.

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 UKF-Based Vehicle Pose Estimation under Randomly Occurring Deception Attacks

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xinghua Liu ◽  
Dandan Bai ◽  
Yunling Lv ◽  
Rui Jiang ◽  
Shuzhi Sam Ge
Keyword(s):  
Kalman Filter ◽  
Pose Estimation ◽  
Upper Bound ◽  
Unscented Kalman Filter ◽  
Estimation Error ◽  
Accurate Estimation ◽  
Ground Vehicles ◽  
Ground Vehicle ◽  
Attack Model ◽  
Error Covariance

Considering various cyberattacks aiming at the Internet of Vehicles (IoV), secure pose estimation has become an essential problem for ground vehicles. This paper proposes a pose estimation approach for ground vehicles under randomly occurring deception attacks. By modeling attacks as signals added to measurements with a certain probability, the attack model has been presented and incorporated into the existing process and measurement equations of ground vehicle pose estimation based on multisensor fusion. An unscented Kalman filter-based secure pose estimator is then proposed to generate a stable estimate of the vehicle pose states; i.e., an upper bound for the estimation error covariance is guaranteed. Finally, the simulation and experiments are conducted on a simple but effective single-input-single-output dynamic system and the ground vehicle model to show the effectiveness of UKF-based secure pose estimation. Particularly, the proposed scheme outperforms the conventional Kalman filter, not only by resulting in more accurate estimation but also by providing a theoretically proved upper bound of error covariance matrices that could be used as an indication of the estimator’s status.

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.

Covariance matching based adaptive unscented Kalman filter for direct filtering in INS/GNSS integration

2016 ◽  
Vol 120 ◽  
pp. 171-181 ◽  
Author(s):  
Yang Meng ◽  
Shesheng Gao ◽  
Yongmin Zhong ◽  
Gaoge Hu ◽  
Aleksandar Subic
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Covariance Matching ◽  
Adaptive Unscented Kalman Filter
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