Publication: Data fusion with indirect kalman filter
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Issued Date
2010
Resource Type
File Type
application/pdf
ISSN
9720871
Other identifier(s)
2-s2.0-78649839970
Rights Holder(s)
Scopus
Bibliographic Citation
Far East Journal of Mathematical Sciences. Vol 45, No.2 (2010), p.223-230
Suggested Citation
Panich S. Data fusion with indirect kalman filter. Far East Journal of Mathematical Sciences. Vol 45, No.2 (2010), p.223-230. Retrieved from: https://hdl.handle.net/20.500.14740/7527
Author(s)
Abstract
The end result of the Wiener solution of the optimal filter problem is a filter weighting function in the continuous case or a set of weight factors in the corresponding discrete problem. In effect, the past values of the input should be weighted in order to determine the present value of the output, that is, the optimal estimate. The most successful applications of Kalman filtering are to optimal about some nominal trajectory in state space that does not depend on the measurement data. The resulting filter is usually referred to as simply a linearized Kalman filter. This study introduced mainly indirect Kalman filter to estimate robot's position. A developed differential encoder system integrated light intensity system is experimental tested in square shape. Experimental results confirmed that indirect Kalman filter improves the accuracy and confidence of position estimation. © 2010 Pushpa Publishing House.