.png){kind=icon}
Return
A finite-difference method for linearization in nonlinear estimation algorithms
.png){kind=icon}
PRE
AI
DOI：10.1016/S0005-1098(97)00127-1.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Linearizations of nonlinear functions that are based on Jacobian matrices often cannot be applied in practical applications of nonlinear estimation techniques. An alternative linearization method is presented in this paper. The method assumes that covariance matrices are determined on a square root factored form. A factorization of the output covariance from a nonlinear vector function is directly determined by perturbing the nonlinear function with the columns of the factored input covariance, without explicitly calculating the linearization and with no differentiations involved. The output covariance is more accurate than that obtained with the ordinary Jacobian linearization method. It also has an advantage that Jacobian matrices do not have to be derived symbolically. (C) 1997 Elsevier Science Ltd.
Keywords:
covariance matrices
estimation algorithms
extended Kalman filters
factorization methods
nonlinear filters
Jacobian matrices
linearization
parameter estimation
recursive estimation
state estimation
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
5.9
Papers:
1.2W
Citations:
5.2W
Organization
No organization information available
.png){kind=icon}
