A Robust Interacting Multiple Model Smoother with Heavy-Tailed Measurement Noises

被引：0

作者：

Cui, Shuai ^{[1
]}

Li, Zhi ^{[1
]}

Yang, Yanbo ^{[1
]}

机构：

[1] Xidian Univ, Sch Mechanoelect Engn, Xian, Peoples R China

来源：

2020 CHINESE AUTOMATION CONGRESS (CAC 2020) | 2020年

关键词：

IMM smoother; Student's t-distribution; heavy-tailed measurement noises; forward and backward filtering; ALGORITHM;

D O I：

10.1109/CAC51589.2020.9327048

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Interacting multiple model (IMM) estimator has found an increasingly wide utilization in the filed of target tracking. Smoothing, uses all obtained measurements to estimate the previous state in order to provide a better estimation. However, IMM smoothers do not perform well and suffer severe performance degradation with some outliers existing in the measurement noises. This paper proposes a novel robust IMM smoother to deal with heavy-tailed measurement noises obeying the Student's t-distribution. The proposed smoother is derived from a forward filter combing with a backward filter. An example of maneuvering target tracking with heavy-tailed

引用

页码：3574 / 3578

页数：5

共 31 条

[21] Robust cooperative target tracking under heavy-tailed non-Gaussian localization noise [J].

Chen X.-B. ;

Chen L. ;

Liang S.-R. ;

Hu Y. .

Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science), 2022, 56 (05) :967-976

[22] A new robust ELM method based on a Bayesian framework with heavy-tailed distribution and weighted likelihood function [J].

Ning, Kefeng ;

Liu, Min ;

Dong, Mingyu .

NEUROCOMPUTING, 2015, 149 :891-903

[23] Parameter Estimation of Heavy-Tailed AR Model With Missing Data Via Stochastic EM [J].

Liu, Junyan ;

Kumar, Sandeep ;

Palomar, Daniel P. .

IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2019, 67 (08) :2159-2172

[24] Robust Identification of Nonlinear State-Space System Based on Dual Heavy-Tailed Noise Distributions [J].

Liu, Xin ;

Hai, Yang ;

Dai, Wei .

Tien Tzu Hsueh Pao/Acta Electronica Sinica, 2024, 52 (09) :3052-3064

[25] MMF-GSTIW-PMBM Adaptive Filter for Multiple Group Target Tracking With Heavy-Tailed Noise [J].

Xue, Xirui ;

Huang, Shucai ;

Wei, Daozhi .

IEEE SENSORS JOURNAL, 2023, 23 (17) :19959-19973

[26] A Novel GMFO-Based Identification Method for MIMO Hammerstein Model with Heavy-Tailed Noise [J].

Wang, Jiaqi ;

Ling, Fuyu .

NEURAL PROCESSING LETTERS, 2025, 57 (03)

[27] Robust identification of linear parameter-varying dual-rate system with non-stationary heavy-tailed noise [J].

Chen, Xiang ;

Li, Ke ;

Liu, Fei .

JOURNAL OF PROCESS CONTROL, 2025, 153

[28] Robust Interacting Multiple Model With Modeling Uncertainties for Maneuvering Target Tracking [J].

Youn, Wonkeun ;

Myung, Hyun .

IEEE ACCESS, 2019, 7 :65427-65443

[29] Robust Bayesian hypocentre and uncertainty region estimation: the effect of heavy-tailed distributions and prior information in cases with poor, inconsistent and insufficient arrival times [J].

Martinsson, J. .

GEOPHYSICAL JOURNAL INTERNATIONAL, 2013, 192 (03) :1156-1178

[30] Interacting Multiple Model Estimation-based Adaptive Robust Unscented Kalman Filter [J].

Gao, Bingbing ;

Gao, Shesheng ;

Zhong, Yongmin ;

Hu, Gaoge ;

Gu, Chengfan .

INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS, 2017, 15 (05) :2013-2025

← 1 2 3 4 →