Open AccessA Method of Stable Extended Kalman filter
Author(s) -
Peng Gu,
Kai Wang,
Shicang Zhang
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/569/3/032018
Subject(s) - invariant extended kalman filter , extended kalman filter , control theory (sociology) , alpha beta filter , fast kalman filter , kalman filter , kernel adaptive filter , ensemble kalman filter , mathematics , divergence (linguistics) , covariance intersection , adaptive filter , filter (signal processing) , convergence (economics) , covariance , computer science , positive definite matrix , stability (learning theory) , covariance matrix , algorithm , filter design , moving horizon estimation , eigenvalues and eigenvectors , artificial intelligence , statistics , philosophy , economic growth , linguistics , control (management) , quantum mechanics , machine learning , computer vision , physics , economics
The extend kalman filter provides an stability solution to the approximate nonlinear-Gaussian filter problem. Due to the limitation of the word length of the processor, the rounding errors is easy to occur in the calculation, which causes the covariance matrix to lose its positive definite and convergence. In order to solve this problem, The proposed stable extended kalman filter (SEKF) method is applied to the problem of non-positive and divergence in recursive filter. This method introduces the matrix decomposition and constructs a covariance square root adaptive filter which is used to solve the numerical stability problem of the extend kalman filter. Experimental results show that SEKF can effectively guarantee symmetric positive definite in recursive calculation, and perform remarkably better than EKF algorithm.