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Informations générales |
| Auteur |
Rhudy, Matthew; Gu, Yu; Napolitano, Marcello R. |
| Publié |
InTech Open Access Publisher, 2013
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| Abstract |
The transformation of the mean and variance of
a normally distributed random variable was considered
through three different nonlinear functions: sin(x), cos(x),
and xk, where k is a positive integer. The true mean and
variance of the random variable after these
transformations is theoretically derived within, and
verified with respect to Monte Carlo experiments. These
statistics are used as a reference in order to compare the
accuracy of two different linearization techniques:
analytical linearization used in the Extended Kalman
Filter (EKF) and statistical linearization used in the
Unscented Kalman Filter (UKF). This comparison
demonstrated the advantage of using the unscented
transformation in estimating the mean after transforming
through each of the considered nonlinear functions.
However, the variance estimation led to mixed results in
terms of which linearization technique provided the best
performance. As an additional analysis, the unscented
transformation was evaluated with respect to its primary
scaling parameter. A nonlinear filtering example is
presented to demonstrate the usefulness of the
theoretically derived results. |
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International Journal of Advanced Robotic Systems
Auteur: Ottaviano, Erika; Ceccarelli, Marco; Husty, Manfred; Yu, Sung-Hoon; Kim, Yong-Tae; Park, Chang-Woo; Hyun, Chang-Ho; Chen, Xiulong; Feng, Weiming; Sun, Xianyang; Gao, Qing; Grigorescu, Sorin M.; Pozna, Claudiu; Liu, Wanli; Zhankui, Wang; Guo, Meng; Fu, Guoyu; Zhang, Jin; Chen, Wenyuan; Peng, Fengchao; Yang, Pei; Chen, Chunlin; Ding, Rui; Yu, Junzhi; Yang, Qinghai; Tan, Min; Polden, Joseph; Pan, [...]
Publié: 2004
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