DocumentCode
698335
Title
Particle filtering for quantized sensor information
Author
Karlsson, Rickard ; Gustafsson, Fredrik
Author_Institution
Dept. of Electr. Eng., Linoping Univ., Linköping, Sweden
fYear
2005
fDate
4-8 Sept. 2005
Firstpage
1
Lastpage
4
Abstract
The implication of quantized sensor information on filtering problems is studied. The Cramér-Rao lower bound (CRLB) is derived for estimation and filtering on quantized data. A particle filter (PF) algorithm that approximates the optimal nonlinear filter is provided, and numerical experiments show that the PF attains the CRLB, while second-order optimal Kalman filter (KF) approaches can perform quite bad.
Keywords
nonlinear filters; particle filtering (numerical methods); quantisation (signal); Cramer-Rao lower bound; optimal nonlinear filter; particle filtering; quantized sensor information; Approximation methods; Atmospheric measurements; Estimation; Kalman filters; Noise measurement; Particle measurements; Quantization (signal);
fLanguage
English
Publisher
ieee
Conference_Titel
Signal Processing Conference, 2005 13th European
Conference_Location
Antalya
Print_ISBN
978-160-4238-21-1
Type
conf
Filename
7077918
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