Strong tracking square-root modified sliding-window variational adaptive Kalman filtering with unknown noise covariance matrices

Qiao, Shuanghu; Fan, Yunsheng; Wang, Guofeng; Mu, Dongdong; He, Zhiping

doi:10.1016/j.sigpro.2022.108837

Cited by 7 publications

(2 citation statements)

References 35 publications

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“…The state vector is positioned as the position and speed of the permanent magnet maglev train, and the sensor collects the position of the point object according to Eq (10). The target states are position and velocity, X ¼ ½x; _ x� T , and the CV model and CA model are Eqs (32) and (33), respectively.…”

Section: Simulationmentioning

confidence: 99%

“…The innovation-based adaptive Kalman filter (IAKF) is a maximum likelihood method that estimates the noise covariance matrix based on the fact that the innovation sequence of the Kalman filter is a white process [ 30 , 31 ]. The multi-model adaptive Kalman filter is an approximation of the Bayesian approach that solves the problem of model uncertainty by combining Kalman filters of different models into a group [ 32 , 33 ]. Kalman algorithm is more common in the previous studies of maglev train operation.…”

Section: Introductionmentioning

confidence: 99%

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Multi-sensor information fusion localization of rare-earth suspended permanent magnet maglev trains based on adaptive Kalman algorithm

Xu,

Fan,

et al. 2023

PLoS ONE

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Since the positioning accuracy of sensors degrades due to noise and environmental interference when a single sensor is used to localize a suspended rare-earth permanent magnetically levitated train, a multi-sensor information fusion method using multiple sensors and self-correcting weighting is proposed for permanent magnetic levitated train localization. A decay memory factor is introduced to reduce the weight of the influence of historical measurement data on the fusion estimation, thus enhancing the robustness of the fusion algorithm. The Kalman filtering results suffer from inaccuracy when process noise is present in the system. In this paper, we use a covariance adaptive scheme that replaces the prediction step of the Kalman filter with covariance. It uses the covariance adaptive scheme to search the posterior sequence online and reconstruct the prior error covariance. Since the process noise covariance is not used in the new adaptive scheme, the negative impact of the mismatch noise statistics is greatly reduced. Simulation and experimental results show that the use of multi-sensor information fusion and covariance adaptive Kalman algorithm has significant advantages in terms of adaptability, accuracy and simplicity.

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Section: Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-sensor information fusion localization of rare-earth suspended permanent magnet maglev trains based on adaptive Kalman algorithm

Xu,

Fan,

et al. 2023

PLoS ONE

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State of Charge Estimation of Lithium‐Ion Batteries with Adaptive Square Root Central Difference Kalman Filter

Du,

Yuan,

Zheng

et al. 2024

Advcd Theory and Sims

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Lithium‐ion batteries have been a major energy source in electric vehicles because of their strong adaptability in operating conditions. Accurate estimation of state of charge (SOC) for lithium‐ion batteries can efficiently improve the efficiency of battery energy utilization. However, SOC estimation is complicated in operating conditions with unknown model parameters. This study proposes an adaptive square root central difference Kalman filter (ASRCDKF) algorithm based on the equivalent circuit model to achieve high‐precision estimation of SOC. First of all, to avoid an open‐circuit voltage test, a linear Kalman filter is constructed to realize real‐time estimation of unknown parameters in the measurement equation. Then, to improve the stability of the algorithm, a square root method is used to ensure a positive semi‐definite of the error covariance matrix that is based on the adaptive central difference Kalman filter algorithm. Model parameters are considered as the state to be estimated, and the joint estimation of the model parameters and SOC is realized by an ASRCDKF algorithm. After that, the linear Kalman filter is coupled with the ASRCDKF to realize the accurate estimation of SOC in the case of both the state equation and the measurement equation including unknown parameters. Last, the ASRCDKF algorithm is compared with the adaptive central difference Kalman filter algorithm and the adaptive cubature Kalman filter algorithm under two sets of operating conditions. The results show that the SOC estimation of the ASRCDKF algorithm is more significantly accurate than other algorithms under different operating conditions.

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Tracking Analysis of the $$\ell _0$$-LMS Algorithm

da Silva,

de Barros,

Pinto

et al. 2024

Circuits Syst Signal Process

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Strong tracking square-root modified sliding-window variational adaptive Kalman filtering with unknown noise covariance matrices

Cited by 7 publications

References 35 publications

Multi-sensor information fusion localization of rare-earth suspended permanent magnet maglev trains based on adaptive Kalman algorithm

Multi-sensor information fusion localization of rare-earth suspended permanent magnet maglev trains based on adaptive Kalman algorithm

State of Charge Estimation of Lithium‐Ion Batteries with Adaptive Square Root Central Difference Kalman Filter

Tracking Analysis of the $$\ell _0$$-LMS Algorithm

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