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A Nonlinear System State Estimation Method Based on Adaptive Fusion of Multiple Kernel Functions

Daxing Xu, Aiyu Hu, Xuelong Han, Lu Zhang, Carlos Aguilar-Ibanez
2021 Complexity  
This paper proposes a state estimation method based on adaptive fusion of multiple kernel functions to improve the accuracy of system state estimation.  ...  Then, the state of the system and the weight of the kernel functions are put together to form an augmented state vector, which can be estimated in real time by using high-degree cubature Kalman filter.  ...  high-degree cubature Kalman filter to estimate the fusion coefficient and the system state in real time, a nonlinear system state estimation method based on adaptive fusion of multiple functions is proposed  ... 
doi:10.1155/2021/5124841 fatcat:4jthuuklfngbxmltjiux34k44q

Kalman filter applied in underwater integrated navigation system

Yan Xincun, Ouyang Yongzhong, Sun Fuping, Fan Hui
2013 Geodesy and Geodynamics  
This paper introduces the Kalman filter as the most useful information fusion technology , and then gives a summary of the Kalman filter applied in underwater integrated navigation system at present, and  ...  For the underwater integrated navigation system, information fusion is an important technology.  ...  The expansion of the system and the nonlinear observer widen the scope of the Kalman ftlter theory. Extended Kalman filter ( EKF) is a most widely used non-linear filtering method.  ... 
doi:10.3724/sp.j.1246.2013.01046 fatcat:jfvnoeyf3batjmieledkbyymie

Tire Road Friction Coefficient Estimation: Review and Research Perspectives

Yan Wang, Jingyu Hu, Fa'an Wang, Haoxuan Dong, Yongjun Yan, Yanjun Ren, Chaobin Zhou, Guodong Yin
2022 Chinese Journal of Mechanical Engineering  
This review provides a comparative analysis of these methods and describes their strengths and weaknesses. Moreover, some future research directions regarding TRFC estimation are presented.  ...  Therefore, accurate knowledge of TRFC contributes to the optimization of driver maneuvers for further improving the safety of intelligent vehicles.  ...  To reduce the workload required for mathematical derivations of the Kalman filtering method, a nonlinear state observer was proposed to estimate TRFC [108, 109] .  ... 
doi:10.1186/s10033-021-00675-z fatcat:3zxr5ezxy5djrp6pehoobropxm

Contribution in Information Signal Processing for Solving State Space Nonlinear Estimation Problems

Hamza Benzerrouk, Alexander Nebylov, Hassen Salhi
2013 Journal of Signal and Information Processing  
The problem of linear and nonlinear filters such as Kalman Filter (KF) and Extended Kalman Filter (EKF) is stated.  ...  Finally, in order to compare these different nonlinear filters, special applications are analyzed by using the proposed techniques to estimate two well-known mathematical state space models, which are  ...  In 1970, Kalman and Bucy introduced extended Kalman filter for nonlinear estimation.  ... 
doi:10.4236/jsip.2013.44048 fatcat:fltb4j2vybh5thlagqfy7vt25u

Simplex Cubature Kalman-Consensus Filter for Distributed Space Target Tracking

Zhaoming Li, Wenge Yang, Dan Ding, Yurong Liao
2018 Wireless Communications and Mobile Computing  
By means of the statistical linear regression method, the posterior mean, covariance, and the transmitted messages in the extended Kalman-consensus filter are approximated using the deduced simplex cubature  ...  A simplex cubature Kalman-consensus filter, which is suitable for distributed space target tracking using multiple radars, is proposed to improve the target tracking accuracy.  ...  The extended Kalman filter (EKF) [5] is the most widely used nonlinear Kalman filter in the past several decades, it uses the multidimensional Taylor series expansion to linearize the nonlinear function  ... 
doi:10.1155/2018/1476426 fatcat:ysprvih4onfr5o7ciikwdi4v6i

Multirate multisensor data fusion for linear systems using Kalman filters and a neural network

Sajjad Safari, Faridoon Shabani, Dan Simon
2014 Aerospace Science and Technology  
The state vector is estimated with a neural network that fuses the outputs of multiple Kalman filters, one filter for each sensor system.  ...  The state estimate is shown to perform better than other data fusion approaches due to the new neural network based sensor fusion approach.  ...  Conflict of interest statement The authors confirm that there are no known conflicts of interest associated with this publication and that there has been no significant financial support for this work  ... 
doi:10.1016/j.ast.2014.06.005 fatcat:spy6gxfqznhupoy2tsgva6sqvu

A survey on distributed filtering, estimation and fusion for nonlinear systems with communication constraints: new advances and prospects

Zhibin Hu, Jun Hu, Guang Yang
2020 Systems Science & Control Engineering  
Second, the recent advances of distributed fusion algorithms for nonlinear systems are discussed, which include distributed fusion extended Kalman filtering, distributed fusion unscented Kalman filtering  ...  Subsequently, some new distributed filtering, estimation and fusion algorithms for nonlinear systems subject to communication constraints are summarized and discussed.  ...  Distributed fusion EKF The extended Kalman filtering (EKF) algorithm has been widely used as a classic nonlinear filtering method (Hu, Wang, Gao, et al., 2012; James & Petersen, 1998; Xiong et al., 2008  ... 
doi:10.1080/21642583.2020.1737846 fatcat:e7nmy2fzkbekvg43pmd63kwqk4

Nonlinear information filtering for distributed multisensor data fusion

Benjamin Noack, Daniel Lyons, Matthias Nagel, Uwe D. Hanebeck
2011 Proceedings of the 2011 American Control Conference  
Essentially, it is an algebraical reformulation of the Kalman filter and provides estimates on the information about an uncertain state rather than on a state itself.  ...  correspond to the fusion and weighting of information.  ...  ACKNOWLEDGMENTS This work was partially supported by the German Research Foundation (DFG) within the Research Training Group GRK 1194 "Self-organizing Sensor-Actuator-Networks".  ... 
doi:10.1109/acc.2011.5991535 fatcat:ml55tkd6knfmhmpmhotbg33cri

EEG-fMRI Fusion: Adaptations of the Kalman Filter for Solving a High-Dimensional Spatio-Temporal Inverse Problem [chapter]

Thomas Deneux
2011 Adaptive Filtering  
In a third part, we will address the problem of strong nonlinearities: we present a modification of the Kalman-based algorithm, and also call for the development of new, more flexible, methods based for  ...  Thus, we will present some new developments that we issued, in particular, the design of a variation of the Kalman filter and smoother which performs a bi-directional sweep, first backward and second forward  ...  We would like to use the extended Kalman filter and smoother (Arulampalam et al., 2002; Welch & Bishop, 2006) to estimate the hidden state.  ... 
doi:10.5772/16490 fatcat:tmybqhbtozdjdltcf6n25ynpiq

Estimation, filtering and fusion for networked systems with network-induced phenomena: New progress and prospects

Jun Hu, Zidong Wang, Dongyan Chen, Fuad E. Alsaadi
2016 Information Fusion  
Secondly, the developments of the estimation, filtering and fusion for networked systems from four aspects (linear networked systems, nonlinear networked systems, complex networks and sensor networks)  ...  In this paper, some recent advances on the estimation, filtering and fusion for networked systems are reviewed.  ...  For example, by using the Riccati-like difference equation approach, the extended Kalman filter has been designed in [16] for a class of time-varying networked systems with stochastic nonlinearities  ... 
doi:10.1016/j.inffus.2016.01.001 fatcat:vu75iw6e5nfatku5jrlcbmg5um

Robust Adaptive Unscented Particle Filter

Li Xue, Shesheng Gao, Yongmin Zhong
2013 International Journal of Intelligent Mechatronics and Robotics  
It also uses the unscented transformation to improve the accuracy of particle filtering, thus providing the reliable state estimation for improving the performance of robust adaptive filtering.  ...  This paper presents a new robust adaptive unscented particle filtering algorithm by adopting the concept of robust adaptive filtering to the unscented particle filter.  ...  The extended Kalman filtering is a commonly used filtering method to nonlinear systems (Julier, Uhlmann & Durrant-Whyte, 2000; Lefebvre, Bruyninckx & Schutter, 2004) .  ... 
doi:10.4018/ijimr.2013040104 fatcat:bfuh2htpone3rkk67l66xrts4u

Mathematical Approaches in Advanced Control Theories

Baocang Ding, Lihua Xie, Weihai Zhang, Xianxia Zhang, Qiang Ling, Yugeng Xi
2012 Journal of Applied Mathematics  
These contributions include, for example, Kalman filter, robust state estimator, fusion estimator, target tracking filter, and modeling parameter estimation. X.  ...  Han et al. study the convergence of the Gaussian mixture extended-target probability hypothesis density filter and its extended Kalman filtering approximation in mildly nonlinear condition.  ... 
doi:10.1155/2012/984296 fatcat:xommhyimknf2nekfqcda6r4k6e

A Review of Data Fusion Techniques

Afnan Alofi, Anwaar Alghamdi, Razan Alahmadi, Najla Aljuaid, Hemalatha M.
2017 International Journal of Computer Applications  
The process of merging multiple data and knowledge from different sources to represent the object into a regular, accurate, useful, meaningful representation is known as data fusion.  ...  This article summarizes the state of data fusion and compares relevant techniques.  ...  For nonlinear systems, it is best to implement the variant version of Kalman filtering; called the Extended Kalman Filter (EKF).  ... 
doi:10.5120/ijca2017914318 fatcat:rhnuzqriw5dzpiqbrtrf2clkkm

Adaptive Fusion Design Using Multiscale Unscented Kalman Filter Approach for Multisensor Data Fusion

Huadong Wang, Shi Dong
2015 Mathematical Problems in Engineering  
This paper utilizes the real-time, recursive, and optimal estimation characteristics of unscented Kalman filter (UKF), as well as the unique advantages of multiscale wavelet transform decomposition in  ...  A new multiscale UKF-based multisensor data fusion algorithm is proposed by combining the UKF with multiscale signal analysis.  ...  Acknowledgments The authors would like to thank the anonymous reviewers for helpful comments which helped them improve the technical quality of the paper.  ... 
doi:10.1155/2015/854085 fatcat:udm34yfw3bdrdoez2pbwpcgj5i

Distributed Nonlinear Filtering Under Packet Drops and Variable Delays for Robotic Visual Servoing [chapter]

Gerasimos G.
2011 Robot Arms  
Fig. 2 . 2 Fusion of the distributed state estimates with the use of the Extended Information Filter As in the case of the Extended Kalman Filter the local filters which constitute the Extended Information  ...  The Extended Information Filter (EIF) performs fusion of local state vector estimates which are provided by local Extended Kalman Filters, using the Information matrix and the Information state vector  ...  Nowadays, the robot arms are indispensable for automation of factories. Moreover, applications of the robot arms are not limited to the industrial factory but expanded to living space or outer space.  ... 
doi:10.5772/17460 fatcat:xycsvwh5njarxhffdtw5er2ma4
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