Kalman filtering based on the maximum correntropy criterion in the presence of non-Gaussian noise

State estimation in the presence of non-Gaussian noise is discussed. Since the Kalman filter uses only second-order signal information, it is not optimal in non-Gaussian noise environments. The maximum correntropy criterion (MCC) is a new approach to measure the similarity of two random variables us...

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Bibliographic Details
Published in2016 Annual Conference on Information Science and Systems (CISS) pp. 500 - 505
Main Authors Izanloo, Reza, Fakoorian, Seyed Abolfazl, Yazdi, Hadi Sadoghi, Simon, Dan
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2016
Subjects
Adaptive filters
Kalman filter
Kalman filters
Kernel
maximum correntropy criterion (MCC)
Noise measurement
non-Gaussian noise
Random variables
State estimation
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Summary:State estimation in the presence of non-Gaussian noise is discussed. Since the Kalman filter uses only second-order signal information, it is not optimal in non-Gaussian noise environments. The maximum correntropy criterion (MCC) is a new approach to measure the similarity of two random variables using information from higher-order signal statistics. The correntropy filter (C-Filter) uses the MCC for state estimation. In this paper we first improve the performance of the C-Filter by modifying its derivation to obtain the modified correntropy filter (MC-Filter). Next we use the MCC and weighted least squares (WLS) to propose an MCC filter in Kalman filter form, which we call the MCC-KF. Simulation results show the superiority of the MCC-KF compared with the C-Filter, the MC-Filter, the unscented Kalman filter, the ensemble Kalman filter, and the Gaussian sum filter, in the presence of two different types of non-Gaussian disturbances (shot noise and Gaussian mixture noise).
DOI:10.1109/CISS.2016.7460553