Next Article in Journal
Langevin Dynamics with Variable Coefficients and Nonconservative Forces: From Stationary States to Numerical Methods
Next Article in Special Issue
Entropy Measures for Stochastic Processes with Applications in Functional Anomaly Detection
Previous Article in Journal
Quantum Information: What Is It All About?
Previous Article in Special Issue
Real-Time Robust Voice Activity Detection Using the Upper Envelope Weighted Entropy Measure and the Dual-Rate Adaptive Nonlinear Filter
Open AccessArticle

Maximum Correntropy Criterion Kalman Filter for α-Jerk Tracking Model with Non-Gaussian Noise

1
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 / Revised: 25 November 2017 / Accepted: 26 November 2017 / 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, α -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 α -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 α -jerk model. View Full-Text
Keywords: Kalman filter; α-jerk model; maximum correntropy criterion; non-Gaussian noise; robustness; influence function; kernel size Kalman filter; α-jerk model; maximum correntropy criterion; non-Gaussian noise; robustness; influence function; kernel size
Show Figures

Figure 1

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.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map

1
Back to TopTop