scholarly journals Error Propagation of Capon’s Minimum Variance Estimator

2021 ◽  
Vol 9 ◽  
Author(s):  
S. Toepfer ◽  
Y. Narita ◽  
D. Heyner ◽  
U. Motschmann

The error propagation of Capon’s minimum variance estimator resulting from measurement errors and position errors is derived within a linear approximation. It turns out, that Capon’s estimator provides the same error propagation as the conventionally used least square fit method. The shape matrix which describes the location depence of the measurement positions is the key parameter for the error propagation, since the condition number of the shape matrix determines how the errors are amplified. Furthermore, the error resulting from a finite number of data samples is derived by regarding Capon’s estimator as a special case of the maximum likelihood estimator.

1981 ◽  
Vol 8 (5) ◽  
pp. 695-702 ◽  
Author(s):  
Michael H. Buonocore ◽  
William R. Brody ◽  
Albert Macovski

Author(s):  
Jennifer L. Bonniwell ◽  
Susan C. Schneider ◽  
Edwin E. Yaz

This work elucidates another theoretical property of the ubiquitous extended Kalman filter by analyzing the energy gain of the continuous-time extended Kalman filter used as a nonlinear observer in the presence of finite-energy disturbances. The analysis provides a bound on the ratio of estimation error energy to disturbance energy, which shows that the extended Kalman filter inherently has the H∞-property along with being the locally optimal minimum variance estimator. A special case of this result is also shown to be the H2-property of the extended Kalman filter.


2008 ◽  
Vol 26 (4) ◽  
pp. 609-621 ◽  
Author(s):  
A. Speranzon ◽  
C. Fischione ◽  
K. Johansson ◽  
A. Sangiovanni-Vincentelli

Sign in / Sign up

Export Citation Format

Share Document