Non-Gaussian noise may degrade the performance of the Kalman filter because the Kalman filter uses only second-order statistical information, so it is not optimal in non-Gaussian noise environments. Also, many systems include equality or inequality state constraints that are not directly included in the system model, and thus are not incorporated in the Kalman filter. To address these combined issues, we propose a robust Kalman-type filter in the presence of non-Gaussian noise that uses information from state constraints. The proposed filter, called the maximum correntropy criterion constrained Kalman filter (MCC-CKF), uses a correntropy metric to quantify not only second-order information but also higher-order moments of the non-Gaussian process and measurement noise, and also enforces constraints on the state estimates. We analytically prove that our newly derived MCC-CKF is an unbiased estimator and has a smaller error covariance than the standard Kalman filter under certain conditions. Simulation results show the superiority of the MCC-CKF compared with other estimators when the system measurement is disturbed by non-Gaussian noise and when the states are constrained.
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ASME 2017 Dynamic Systems and Control Conference
October 11–13, 2017
Tysons, Virginia, USA
Conference Sponsors:
- Dynamic Systems and Control Division
ISBN:
978-0-7918-5828-8
PROCEEDINGS PAPER
Maximum Correntropy Criterion Constrained Kalman Filter
Seyed Fakoorian,
Seyed Fakoorian
Cleveland State University, Cleveland, OH
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Mahmoud Moosavi,
Mahmoud Moosavi
Cleveland State University, Cleveland, OH
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Reza Izanloo,
Reza Izanloo
Cleveland State University, Cleveland, OH
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Vahid Azimi,
Vahid Azimi
Cleveland State University, Cleveland, OH
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Dan Simon
Dan Simon
Cleveland State University, Cleveland, OH
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Seyed Fakoorian
Cleveland State University, Cleveland, OH
Mahmoud Moosavi
Cleveland State University, Cleveland, OH
Reza Izanloo
Cleveland State University, Cleveland, OH
Vahid Azimi
Cleveland State University, Cleveland, OH
Dan Simon
Cleveland State University, Cleveland, OH
Paper No:
DSCC2017-5326, V002T04A008; 7 pages
Published Online:
November 14, 2017
Citation
Fakoorian, S, Moosavi, M, Izanloo, R, Azimi, V, & Simon, D. "Maximum Correntropy Criterion Constrained Kalman Filter." Proceedings of the ASME 2017 Dynamic Systems and Control Conference. Volume 2: Mechatronics; Estimation and Identification; Uncertain Systems and Robustness; Path Planning and Motion Control; Tracking Control Systems; Multi-Agent and Networked Systems; Manufacturing; Intelligent Transportation and Vehicles; Sensors and Actuators; Diagnostics and Detection; Unmanned, Ground and Surface Robotics; Motion and Vibration Control Applications. Tysons, Virginia, USA. October 11–13, 2017. V002T04A008. ASME. https://doi.org/10.1115/DSCC2017-5326
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