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
收藏
页码：3574 / 3578
页数：5
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