DocumentCode :
3175674
Title :
The two-state estimator for linear systems with additive measurement and process Cauchy noise
Author :
Speyer, Jason L. ; Idan, Moshe ; Fernandez, J.
Author_Institution :
Mech. & Aerosp. Eng., Univ. of California, Los Angeles, Los Angeles, CA, USA
fYear :
2012
fDate :
10-13 Dec. 2012
Firstpage :
4107
Lastpage :
4114
Abstract :
The conditional mean estimator for a two-state linear system with additive Cauchy measurement and process noises is developed. Although the Cauchy densities that model the process and measurement noise have an undefined first moment and an infinite second moment, the probability density function conditioned on linear noisy measurements does have a finite mean and variance. The conditional probability density function (cpdf) given the measurement history appears to be difficult to compute directly. However, the characteristic function of the unnormalized cpdf can be sequentially propagated through measurement updates and dynamic state propagation. A key step in processing a measurement lies in obtaining a closed form solution of an associated convolution integral. The solution obtained in this work is presented as a sum of terms all contributing to the structure of the characteristic function of the unnormalized cpdf, where the number of terms grows arithmetically with time. Many of these terms have a negligible contribution and can therefore be pruned. Once this characteristic is obtained, the mean and variance are easily computed from the first and second derivatives of the characteristic function, evaluated at the origin in the spectral variables´ domain. A two-state dynamic system example numerically demonstrates the Cauchy estimator´s performance compared with that of a Kalman filter for simulations using both Cauchy and Gaussian noises.
Keywords :
convolution; linear systems; probability; signal processing; Cauchy density; additive measurement noise; conditional mean estimator; conditional probability density function; convolution integral; dynamic state propagation; infinite second moment; linear noisy measurement; measurement updates; process Cauchy noise; spectral variable; two-state dynamic system; two-state estimator; two-state linear system; unnormalized cpdf; Convolution; Mathematical model; Noise; Noise measurement; Probability density function; Stochastic processes; Time measurement;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Decision and Control (CDC), 2012 IEEE 51st Annual Conference on
Conference_Location :
Maui, HI
ISSN :
0743-1546
Print_ISBN :
978-1-4673-2065-8
Electronic_ISBN :
0743-1546
Type :
conf
DOI :
10.1109/CDC.2012.6426632
Filename :
6426632
Link To Document :
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