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An efficient tuning framework for Kalman filter parameter optimization using design of experiments and genetic algorithms

Alan Zhang
Alan Zhang
¹Department of Systems and Computer Engineering, Carleton University Embedded and Multi-sensor Systems Lab, Carleton University, 1125 Colonel By Drive, Ottawa, ON K1S 5B6, Canada
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- For correspondence: [email protected]
and Mohamed Maher Atia
Mohamed Maher Atia
²Department of Systems and Computer Engineering, Carleton University Embedded and Multi-sensor Systems Lab, Carleton University, 1125 Colonel By Drive, Ottawa, ON K1S 5B6, Canada

NAVIGATION: Journal of the Institute of Navigation
December 2020,
67
(4)
775-793;
DOI: https://doi.org/10.1002/navi.399

FIGURE 26

FIGURE 26

RMSE vs 6 selected parameters for 50 randomized parameter configurations

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An efficient tuning framework for Kalman filter parameter optimization using design of experiments and genetic algorithms

Alan Zhang

Alan Zhang

and Mohamed Maher Atia

Mohamed Maher Atia

Institute of Navigation

Dec 2020,

67

(4)

775-

793;

DOI: 10.1002/navi.399

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