Results 181 to 190 of about 18,880 (196)

Performance Analysis of UKF for Nonlinear Problems

2009 Third International Symposium on Intelligent Information Technology Application, 2009
Unscented Kalman filter (UKF) is a class of nonlinear filtering methods based on unscented transform within the Kalman filter framework. It is in light of the intuition that to approximate a probability distribution by a set of deterministic samples is easier than to approximate an arbitrary nonlinear transform.
Guanglin Li, Fuming Sun, Na Cheng
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An adaptive UKF with noise statistic estimator

2009 4th IEEE Conference on Industrial Electronics and Applications, 2009
The normal unscented Kalman filter (UKF) suffers from performance degradation and even divergence while mismatch between the noise distribution assumed to be known as a priori by UKF and the true ones in a real system. In order to improve the performance of the UKF with uncertain or timevarying noise statistic, a novel adaptive UKF with noise statistic
null Lin Zhao, null Xiaoxu Wang
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Effective fault diagnosis based on strong tracking UKF

Aircraft Engineering and Aerospace Technology, 2011
PurposeThe purpose of this paper is to address the flaws of traditional methods and fulfil the special fault‐tolerant re‐entry navigation requirements of reusable boost vehicle (RBV).Design/methodology/approachA kind of improved estimation method based on strong tracking unscented Kalman filter (STUKF) is put forward.
Pengxin Han, Rongjun Mu, Naigang Cui
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A heuristic for sigma set selection of UKF

2014 12th International Conference on Signal Processing (ICSP), 2014
In this paper we present a higher order moment-matching algorithm for computing the distribution parameters of nonlinear transformation random variables. The new algorithm has two distinct aspects compared to the standard Unscented Kalman Filter (UKF).
Yujin Wang, Jiang Liu, Wenqiang Yang, Ju Zhang   +3 more
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PF-UKF-RJMCMC Approaches for Radar Target-Tracking

2009 International Conference on Information Technology and Computer Science, 2009
Nonlinear problem of maneuvering target is a hot and difficult topic in radar target tracking fielding. This paper outline the the pros and cons of non-linear filtering methods nowdays, emphatically analyses uncertainty sampling and random sampling method, describe Markov chain Monte Carlo algorithms, along with Reversible Jump ratio improving methods.
Zhao Huibo, Pan Quan
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An Adaptive UKF Algorithm for Process Fault Prognostics

2009 Second International Conference on Intelligent Computation Technology and Automation, 2009
For standard unscented Kalman filters (UKF), the unknown covariance matrices of prior state estimate error, output prediction error and posterior state estimate error propagate recursively through fixed models, which does not consider the actual distribution information of errors.
Yuping Cao, Xuemin Tian
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GPS/SINS Positioning Method Based on Robust UKF

2012 International Conference on Industrial Control and Electronics Engineering, 2012
As an improved Unscented Kalman Filtering (UKF) Algorithm, the robust UKF is proposed to solve the positioning accuracy problem of GPS/SINS integrated navigation system used in dynamic environment. The robust estimation theory is applied to the standard UKF algorithm to solve the estimation-error problem in the satellite integrated navigation system ...
Qiuting Wang, Duo Xiao
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EKF and UKF based synchronization of hyperchaotic systems

2016 International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT), 2016
Noise like yet deterministic behavior of chaotic systems has led the researchers to show a great interest in developing secure communication schemes based on chaotic systems. In the field of secure communication, hyperchotic systems are preferred to traditional chaotic systems because they have more complicated dynamical behavior.
Sharad Mathur, Bharat Bhushan Sharma
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Maneuvering Target Tracking Based on ANFIS and UKF

2008 International Conference on Intelligent Computation Technology and Automation (ICICTA), 2008
A maneuvering target tracking algorithm is proposed to overcome the defects of poor filtering precision while using unscented Kalman filter (UKF). The method combines the merits of UKF and adaptive neuro-fuzzy inference system (ANFIS). ANFIS is used to adjust system noise covariance matrix in target tracking system. Fuzzy inference, neural networks and
Anfu Zhu, Zhanrong Jing, Weijun Chen, Yan Yang, Anxue Zhang   +4 more
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UKF-SLAM Based Gravity Gradient Aided Navigation

2014
Considering the two characteristics: (1) simultaneous localization and mapping (SLAM) is a popular algorithm for autonomous underwater robot, but visual SLAM is significantly influenced by weak illumination; (2) geomagnetism-aided navigation and gravity-aided navigation are equally important methods in the field of robot navigation, but both are ...
Meng Wu, Ying Weng
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