Maximum Correntropy Criterion Kalman Filter for α-Jerk Tracking Model with Non-Gaussian Noise
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Department of Mathematics and System Science, National University of Defense Technology, Fuyuan Road No. 1, Changsha 410072, China
2
Beijing Institute of Control Engineering, China Academy of Space Technology, Beijing 100080, China
3
Unit 94, PLA 91550, Dalian 116023, China
*
Author to whom correspondence should be addressed.
Entropy 2017, 19(12), 648; https://doi.org/10.3390/e19120648
Received: 15 November 2017
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Revised: 25 November 2017
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Accepted: 26 November 2017
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Published: 29 November 2017
(This article belongs to the Special Issue Entropy in Signal Analysis)
As one of the most critical issues for target track,
-jerk model is an effective maneuver target track model. Non-Gaussian noises always exist in the track process, which usually lead to inconsistency and divergence of the track filter. A novel Kalman filter is derived and applied on
-jerk tracking model to handle non-Gaussian noise. The weighted least square solution is presented and the standard Kalman filter is deduced firstly. A novel Kalman filter with the weighted least square based on the maximum correntropy criterion is deduced. The robustness of the maximum correntropy criterion is also analyzed with the influence function and compared with the Huber-based filter, and, moreover, the kernel size of Gaussian kernel plays an important role in the filter algorithm. A new adaptive kernel method is proposed in this paper to adjust the parameter in real time. Finally, simulation results indicate the validity and the efficiency of the proposed filter. The comparison study shows that the proposed filter can significantly reduce the noise influence for
-jerk model.
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Keywords:
Kalman filter; α-jerk model; maximum correntropy criterion; non-Gaussian noise; robustness; influence function; kernel size
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MDPI and ACS Style
Hou, B.; He, Z.; Zhou, X.; Zhou, H.; Li, D.; Wang, J. Maximum Correntropy Criterion Kalman Filter for α-Jerk Tracking Model with Non-Gaussian Noise. Entropy 2017, 19, 648. https://doi.org/10.3390/e19120648
AMA Style
Hou B, He Z, Zhou X, Zhou H, Li D, Wang J. Maximum Correntropy Criterion Kalman Filter for α-Jerk Tracking Model with Non-Gaussian Noise. Entropy. 2017; 19(12):648. https://doi.org/10.3390/e19120648
Chicago/Turabian StyleHou, Bowen, Zhangming He, Xuanying Zhou, Haiyin Zhou, Dong Li, and Jiongqi Wang. 2017. "Maximum Correntropy Criterion Kalman Filter for α-Jerk Tracking Model with Non-Gaussian Noise" Entropy 19, no. 12: 648. https://doi.org/10.3390/e19120648
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