Erratum for S. Brunke and M. Campbell, "Square Root Sigma Point Filtering for Aerodynamic Model Estimation"

2005 ◽  
Vol 28 (2) ◽  
pp. 383-383
Author(s):  
Shelby Brunke ◽  
Mark E. Campbell
Keyword(s):  
Model Estimation ◽  
Square Root ◽  
Sigma Point ◽  
Aerodynamic Model

Improved square-root cubature information filter

2016 ◽  
Vol 39 (4) ◽  
pp. 579-588 ◽  
Author(s):  
Yulong Huang ◽  
Yonggang Zhang ◽  
Ning Li ◽  
Lin Zhao
Keyword(s):  
Large Scale ◽  
Estimation Error ◽  
Sampling Interval ◽  
Prediction Errors ◽  
Square Root ◽  
Sigma Point ◽  
Theoretical Comparison ◽  
Error Covariance ◽  
Information Filter ◽  
Scale Matrix

In this paper, a theoretical comparison between existing the sigma-point information filter (SPIF) framework and the unscented information filter (UIF) framework is presented. It is shown that the SPIF framework is identical to the sigma-point Kalman filter (SPKF). However, the UIF framework is not identical to the classical SPKF due to the neglect of one-step prediction errors of measurements in the calculation of state estimation error covariance matrix. Thus SPIF framework is more reasonable as compared with UIF framework. According to the theoretical comparison, an improved cubature information filter (CIF) is derived based on the superior SPIF framework. Square-root CIF (SRCIF) is also developed to improve the numerical accuracy and stability of the proposed CIF. The proposed SRCIF is applied to a target tracking problem with large sampling interval and high turn rate, and its performance is compared with the existing SRCIF. The results show that the proposed SRCIF is more reliable and stable as compared with the existing SRCIF. Note that it is impractical for information filters in large-scale applications due to the enormous computational complexity of large-scale matrix inversion, and advanced techniques need to be further considered.

scholarly journals Erratum to: Joseph covariance formula adaptation to Square-Root Sigma-Point Kalman filters

2017 ◽  
Vol 88 (3) ◽  
pp. 1987-1987 ◽  
Author(s):  
Francesco De Vivo ◽  
Alberto Brandl ◽  
Manuela Battipede ◽  
Piero Gili
Keyword(s):  
Kalman Filters ◽  
Square Root ◽  
Sigma Point

Joseph covariance formula adaptation to Square-Root Sigma-Point Kalman filters

2017 ◽  
Vol 88 (3) ◽  
pp. 1969-1986 ◽  
Author(s):  
Francesco De Vivo ◽  
Alberto Brandl ◽  
Manuela Battipede ◽  
Piero Gili
Keyword(s):  
Kalman Filters ◽  
Square Root ◽  
Sigma Point

Square Root Sigma Point Filtering for Real-Time, Nonlinear Estimation

2004 ◽  
Vol 27 (2) ◽  
pp. 314-317 ◽  
Author(s):  
Shelby Brunke ◽  
Mark E. Campbell
Keyword(s):  
Real Time ◽  
Nonlinear Estimation ◽  
Square Root ◽  
Sigma Point

Square-Root Sigma-Point Information Filtering

2012 ◽  
Vol 57 (11) ◽  
pp. 2945-2950 ◽  
Author(s):  
Guoliang Liu ◽  
Florentin Worgotter ◽  
Irene Markelic
Keyword(s):  
Information Filtering ◽  
Square Root ◽  
Sigma Point

scholarly journals Technical Note: Propagating correlations in atmospheric inversions using different Kalman update smoothers

2011 ◽  
Vol 11 (3) ◽  
pp. 921-929 ◽  
Author(s):  
J. Tang ◽  
Q. Zhuang
Keyword(s):  
Trace Gases ◽  
Technical Note ◽  
The Other ◽  
Square Root ◽  
State Variables ◽  
Sigma Point ◽  
Kalman Smoother ◽  
On Line ◽  
Nonlinear Observation ◽  
Atmospheric Inversions

Abstract. The scheme to propagate correlations between on-line and off-line state variables in atmospheric inversions using the fixed-lag Kalman smoother proposed in Bruhwiler et al. (2005) is explained as a process to impose a balanced constraint on the on-line state variables. It is then extended to the fixed-lag ensemble square root Kalman smoother and fixed-lag square root sigma-point Kalman smoother, allowing us to treat nonlinear observation operators easily. Further, to constrain the posterior fluxes within their feasible ranges, the constrained fixed-lag Kalman smoother is presented and the variable transform technique is proposed for the other two smoothers. Comparisons between various methods and observational data are conducted using a synthetic inversion of atmospheric CH4 fluxes. The results indicate that our developed methods are good alternatives to existing methods for conducting sequential inversion of atmospheric trace gases. It is also shown that the benefit to include the correlations between on-line and off-line state variables is case dependent.

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