A Gaussian Mixture Unscented Rauch-Tung-Striebel Smoothing Framework for Trajectory Reconstruction

被引:21
作者
He, Jiacheng [1 ]
Peng, Bei [1 ]
Feng, Zhenyu [1 ]
Zhong, Shan [1 ]
He, Bo [2 ]
Wang, Gang [3 ]
机构
[1] Univ Elect Sci & Technol China, Sch Mech & Elect Engn, Chengdu 611731, Peoples R China
[2] Ocean Univ China, Qingdao, Shandong, Peoples R China
[3] Univ Elect Sci & Technol China, Sch Informat & Commun Engn, Chengdu 611731, Peoples R China
关键词
Gaussian mixture model (GMM); interacting multiple model (IMM); non-Gaussian noise; unscented Kalman filter (UKF); unscented Rauch-Tung-Striebel (URTS) smoother; KALMAN FILTER; SYSTEMS;
D O I
10.1109/TII.2024.3360478
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Trajectory reconstruction (TR) plays an important role in practical applications. The data collected for TR are often contaminated with non-Gaussian noise due to environmental factors, which reduces the accuracy of TR. This study proposes a novel method to suppress the effects of non-Gaussian measurement noise. The method consists of two steps: decomposing the measurement noise and performing a weighted fusion of the relevant states. In the first step, the measurement noise is decomposed into a weighted combination of multiple Gaussian distributions using the Gaussian mixture model. In the second step, the interacting multiple model is employed to perform the weighted fusion of the relevant states. The main idea of the proposed method is to transform the state estimation problem from a non-Gaussian and nonlinear case into a state estimation problem in the nonlinear and Gaussian case. Based on this idea, a new unscented Kalman filter and an unscented Rauch-Tung-Striebel smoother framework are developed. The TR simulations and experiments are conducted for an underwater unmanned vehicle to verify the effectiveness and superiority of the proposed algorithms. The results demonstrate that the performance of the proposed algorithms is significantly better than that of the prominent existing algorithms, especially in the presence of non-Gaussian noise.
引用
收藏
页码:7481 / 7491
页数:11
相关论文
共 31 条
[1]   Cubature Kalman Filters [J].
Arasaratnam, Ienkaran ;
Haykin, Simon .
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2009, 54 (06) :1254-1269
[2]   A Novel Heavy-Tailed Mixture Distribution Based Robust Kalman Filter for Cooperative Localization [J].
Bai, Mingming ;
Huang, Yulong ;
Zhang, Yonggang ;
Chen, Feng .
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2021, 17 (05) :3671-3681
[3]  
Bishop C. M., 2006, Pattern Recognition and Machine Learning
[4]   THE INTERACTING MULTIPLE MODEL ALGORITHM FOR SYSTEMS WITH MARKOVIAN SWITCHING COEFFICIENTS [J].
BLOM, HAP ;
BARSHALOM, Y .
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 1988, 33 (08) :780-783
[5]   Robust Power System State Estimation With Minimum Error Entropy Unscented Kalman Filter [J].
Dang, Lujuan ;
Chen, Badong ;
Wang, Shiyuan ;
Ma, Wentao ;
Ren, Pengju .
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2020, 69 (11) :8797-8808
[6]   A Background-Impulse Kalman Filter With Non-Gaussian Measurement Noises [J].
Fan, Xuxiang ;
Wang, Gang ;
Han, Jiachen ;
Wang, Yinghui .
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2023, 53 (04) :2434-2443
[7]   Model-based trajectory reconstruction with IMM smoothing and segmentation [J].
Garcia, Jesus ;
Besada, Juan A. ;
Molina, Jose M. ;
de Miguel, Gonzalo .
INFORMATION FUSION, 2015, 22 :127-140
[8]   Improved kinematic interpolation for AIS trajectory reconstruction [J].
Guo, Shaoqing ;
Mou, Junmin ;
Chen, Linying ;
Chen, Pengfei .
OCEAN ENGINEERING, 2021, 234
[9]   Generalized minimum error entropy for robust learning [J].
He, Jiacheng ;
Wang, Gang ;
Cao, Kui ;
Diao, He ;
Wang, Guotai ;
Peng, Bei .
PATTERN RECOGNITION, 2023, 135
[10]   Generalized minimum error entropy Kalman filter for non-Gaussian noise [J].
He, Jiacheng ;
Wang, Gang ;
Yu, Huijun ;
Liu, JunMing ;
Peng, Bei .
ISA TRANSACTIONS, 2023, 136 :663-675