卡尔曼滤波器
高斯分布
计算机科学
噪音(视频)
高斯函数
跟踪(教育)
核(代数)
算法
高斯噪声
扩展卡尔曼滤波器
高斯滤波器
集合卡尔曼滤波器
人工智能
控制理论(社会学)
数学
量子力学
教育学
组合数学
图像(数学)
物理
心理学
控制(管理)
作者
Mei Yang,Yaonan Wang,Yang Hong,Badong Chen,Hui Li,Zhihong Jiang
标识
DOI:10.34133/2022/9796015
摘要
Target tracking plays an important role in the construction, operation, and maintenance of the space station by the robot, which puts forward high requirements on the accuracy of target tracking. However, the special space environment may cause complex non-Gaussian noise in target tracking data. And the performance of traditional Kalman Filter will deteriorate seriously when the error signals are non-Gaussian, which may lead to mission failure. In the paper, a novel Kalman Filter algorithm with Generalized Maximum Correntropy Criterion (GMCKF) is proposed to improve the tracking accuracy with non-Gaussian noise. The GMCKF algorithm, which replaces the default Gaussian kernel with the generalized Gaussian density function as kernel, can adapt to multi-type non-Gaussian noises and evaluate the noise accurately. A parameter automatic selection algorithm is proposed to determine the shape parameter of GMCKF algorithm, which helps the GMCKF algorithm achieve better performance for complex non-Gaussian noise. The performance of the proposed algorithm has been evaluated by simulations and the ground experiments. Then, the algorithm has been applied in the maintenance experiments in TianGong-2 space laboratory of China. The results validated the feasibility of the proposed method with the target tracking precision improved significantly in complex non-Gaussian environment.
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