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

作者：Hou, Bowen; He, Zhangming; Zhou, Xuanying; Zhou, Haiyin; Li, Dong; Wang, Jiongqi^*

来源：Entropy, 2017, 19(12): 648.

DOI：10.3390/e19120648

• Kalman filter
• alpha-jerk model
• maximum correntropy criterion
• non-Gaussian noise
• robustness
• influence function
• kernel size

As one of the most critical issues for target track, alpha-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 alpha-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 alpha-jerk model.

• 出版日期2017-12
• 单位北京控制工程研究所; 中国人民解放军国防科学技术大学; 中国空间技术研究院