Research of Optimized Adaptive Kalman Filtering

Xu, Fei; Su, Yongqing; Liu, Hao

doi:10.1109/ccdc.2014.6852351

Cited by 7 publications

(4 citation statements)

References 9 publications

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“…Based on [27], the estimation of R m is as follows:

\begin{equation} {{{\hat{\mathrm R}}}}_m = \frac{1}{m}\left( {\sum_{i = 1}^m {{{\tilde{\mathrm y}}}_i^ - {{\tilde{\mathrm y}}}{{_i^ - }}^{\mathop{\mathrm T}\nolimits} - {{\mathrm{C}}}_{i - 1}{\mathrm{P}}_i^ - {\mathrm{C}}_{i - 1}^{\mathop{\mathrm T}\nolimits} } } \right). \end{equation}

…”

Section: Multi‐rate Event‐triggered Predictive Control Algorithmmentioning

confidence: 99%

Multi‐rate event‐triggered control with imperfect data for dense medium separation

Dai,

Yang,

Zhang

et al. 2023

IET Control Theory & Appl

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The stability control of suspension density is an important means to ensure the quality of fine coal in dense medium separation (DMS) process. This paper focuses on the tracking control issue of the dense medium suspension density in a non‐uniform sampling and networked control environment, and proposes a multi‐rate event trigger control design method. Firstly, lifting technique and multi‐rate identification method are adopted to establish the multi‐rate model. Secondly, a multi‐rate event‐triggered predictive control (MEPC) algorithm is proposed to reduce the waste of communication resources and ensure tracking performance. Furthermore, by using a Kalman filter to eliminate the effects of noise and developing a compensation mechanism to predict the lost data, a robust MEPC (RMEPC) algorithm is further proposed for imperfect data in the actual production process. Finally, the hardware‐in‐the‐loop simulation experiments with actual parameters of coal preparation plant have been carried out, showing the effectiveness of the proposed method.

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“…Based on [27], the estimation of R m is as follows:

\begin{equation} {{{\hat{\mathrm R}}}}_m = \frac{1}{m}\left( {\sum_{i = 1}^m {{{\tilde{\mathrm y}}}_i^ - {{\tilde{\mathrm y}}}{{_i^ - }}^{\mathop{\mathrm T}\nolimits} - {{\mathrm{C}}}_{i - 1}{\mathrm{P}}_i^ - {\mathrm{C}}_{i - 1}^{\mathop{\mathrm T}\nolimits} } } \right). \end{equation}

…”

Section: Multi‐rate Event‐triggered Predictive Control Algorithmmentioning

confidence: 99%

Multi‐rate event‐triggered control with imperfect data for dense medium separation

Dai,

Yang,

Zhang

et al. 2023

IET Control Theory & Appl

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“…If the description of Q and R in practical applications is inaccurate, the filtering estimation error will be very large or even divergent [11], [12]. Therefore, the adaptive Kalman filter based on the innovation sequence is used to accurately estimate Q and R [13], [14] to enhance the stability of the filter.…”

Section: Measurements Updatementioning

confidence: 99%

High-Precision Mobile Robot Localization Using the Integration of RAR and AKF

WANG

Tan

2023

IEICE Trans. Inf. & Syst.

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The high-precision indoor positioning technology has gradually become one of the research hotspots in indoor mobile robots. Relax and Recover (RAR) is an indoor positioning algorithm using distance observations. The algorithm restores the robot's trajectory through curve fitting and does not require time synchronization of observations. The positioning can be successful with few observations. However, the algorithm has the disadvantages of poor resistance to gross errors and cannot be used for real-time positioning. In this paper, while retaining the advantages of the original algorithm, the RAR algorithm is improved with the adaptive Kalman filter (AKF) based on the innovation sequence to improve the antigross error performance of the original algorithm. The improved algorithm can be used for real-time navigation and positioning. The experimental validation found that the improved algorithm has a significant improvement in accuracy when compared to the original RAR. When comparing to the extended Kalman filter (EKF), the accuracy is also increased by 12.5%, which can be used for high-precision positioning of indoor mobile robots.

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“…If inaccurate mathematical models or statistical noise characteristics are used to design the Kalman filter, the performance of the filter will be degraded, resulting in larger estimation errors, and even filter divergence. To solve this problem, various adaptive Kalman filters (AKF) have been produced [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-Fading Factor and Updated Monitoring Strategy Adaptive Kalman Filter-Based Variational Bayesian

Shan

Zhou

Yang

et al. 2020

Sensors

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Aiming at the problem that the performance of adaptive Kalman filter estimation will be affected when the statistical characteristics of the process and measurement of the noise matrices are inaccurate and time-varying in the linear Gaussian state-space model, an algorithm of multi-fading factor and an updated monitoring strategy adaptive Kalman filter-based variational Bayesian is proposed. Inverse Wishart distribution is selected as the measurement noise model and the system state vector and measurement noise covariance matrix are estimated with the variational Bayesian method. The process noise covariance matrix is estimated by the maximum a posteriori principle, and the updated monitoring strategy with adjustment factors is used to maintain the positive semi-definite of the updated matrix. The above optimal estimation results are introduced as time-varying parameters into the multiple fading factors to improve the estimation accuracy of the one-step state predicted covariance matrix. The application of the proposed algorithm in target tracking is simulated. The results show that compared with the current filters, the proposed filtering algorithm has better accuracy and convergence performance, and realizes the simultaneous estimation of inaccurate time-varying process and measurement noise covariance matrices.

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Research of Optimized Adaptive Kalman Filtering

Cited by 7 publications

References 9 publications

Multi‐rate event‐triggered control with imperfect data for dense medium separation

Multi‐rate event‐triggered control with imperfect data for dense medium separation

High-Precision Mobile Robot Localization Using the Integration of RAR and AKF

Multi-Fading Factor and Updated Monitoring Strategy Adaptive Kalman Filter-Based Variational Bayesian

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