The fuzzy Kalman filter: State estimation using possibilistic techniques

A new method to implement fuzzy Kalman filters is introduced. The combination of possibilistic techniques and the extended Kalman filter has special application in fields where inaccurate information is involved. The novelty of this article comes from the fact that by using possibility distributions...

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Published inFuzzy sets and systems Vol. 157; no. 16; pp. 2145 - 2170
Main Authors MATIA, Fernando, JIMENEZ, Agustin, AL-HADITHI, Basil M, RODRIGUEZ-LOSADA, Diego, GALAN, Ramon
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier B.V 16.08.2006
Elsevier
Subjects
Applied sciences
Circuit properties
Computer science; control theory; systems
Control theory. Systems
Digital circuits
Electric, optical and optoelectronic circuits
Electronic circuits
Electronics
Exact sciences and technology
Extended Kalman filter
Fuzzy control
Information, signal and communications theory
Localization
Mathematical methods
Miscellaneous
Possibility distributions
Robotics
State estimation
System theory
Telecommunications and information theory
Theoretical computing
Possibility distributions
Fuzzy control
Localization
State estimation
Extended Kalman filter
Robotics
Fuzzy system
Moving robot
Gaussian distribution
Fuzzy set
Algorithm
Implementation
Engineering
Information processing
Distribution function
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Summary:A new method to implement fuzzy Kalman filters is introduced. The combination of possibilistic techniques and the extended Kalman filter has special application in fields where inaccurate information is involved. The novelty of this article comes from the fact that by using possibility distributions, instead of Gaussian distributions, a fuzzy description of the expected state and observation is sufficient to obtain a good estimation. Some characteristics of this approach are that uncertainty does not need to be symmetric, and that a wide region of possible values for the expectations is allowed. To implement the algorithm, this approach also contributes a method to propagate uncertainty through the process model and the observation model, based on trapezoidal possibility distributions. Finally, several examples of a real mobile robot moving through a localization process, while using qualitative landmarks, are shown.
ISSN:0165-0114
1872-6801
DOI:10.1016/j.fss.2006.05.003