Open document
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General information |
| Author |
Zhang, Xiaoyong; Peng, Jun; Yu, Wentao; Lin, Kuo-chi |
| Published |
InTech Open Access Publisher, 2012
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| Abstract |
Nonlinear object tracking from noisy
measurements is a basic skill and a challenging task of
mobile robotics, especially under dynamic environments.
The particle filter is a useful tool for nonlinear object
tracking with non‐Gaussian noise. Nonlinear object
tracking needs the real‐time processing capability of the
particle filter. While the number in a traditional particle
filter is fixed, that can lead to a lot of unnecessary
computation. To address this issue, a confidence‐levelbased
new adaptive particle filter (NAPF) algorithm is
proposed in this paper. In this algorithm the idea of
confidence interval is utilized. The least number of
particles for the next time instant is estimated according
to the confidence level and the variance of the estimated
state. Accordingly, an improved systematic re‐sampling
algorithm is utilized for the new improved particle filter.
NAPF can effectively reduce the computation while
ensuring the accuracy of nonlinear object tracking. The
simulation results and the ball tracking results of the
robot verify the effectiveness of the algorithm. |
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International Journal of Advanced Robotic Systems
Author: 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, [...]
Published: 2004
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