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Frontiers of Information Technology & Electronic Engineering

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A novel multiple-outlier-robust Kalman filter

Author(s): Yulong HUANG, Mingming BAI, Yonggang ZHANG
Affiliation(s): College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
Corresponding email(s): heuedu@163.com, mingming.bai@hrbeu.edu.cn, zhangyg@hrbeu.edu.cn
Key Words: Kalman filtering; Multiple statistical similarity measure; Multiple outliers; Fixed-point iteration; State estimate

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Yulong HUANG, Mingming BAI, Yonggang ZHANG. A novel multiple-outlier-robust Kalman filter[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.2000642

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author="Yulong HUANG, Mingming BAI, Yonggang ZHANG",
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Abstract
Chinese Summary
Academic Network
Reviewer Comment

Abstract: This paper presents a novel multiple-outlier-robust Kalman filter (MORKF) for linear stochastic discrete-time systems. A new multiple statistical similarity measure is first proposed to evaluate the similarity between two random vectors from dimension to dimension. Then, the proposed MORKF is derived via maximizing a multiple statistical similarity measure based cost function. The MORKF guarantees the convergence of iterations in mild conditions, and the boundedness of the approximation errors is analyzed theoretically. The selection strategy for the similarity function and comparisons with existing robust methods are presented. Simulation results show the advantages of the proposed filter.

一种新型多样野值鲁棒卡尔曼滤波器

黄玉龙，柏明明，张勇刚
哈尔滨工程大学智能科学与工程学院，中国哈尔滨市，150001
摘要：针对线性离散随机系统，提出一种新型多样野值鲁棒卡尔曼滤波器（MORKF）。首先提出一种新的多重统计相似度来衡量两个随机向量各维度之间的相似性。然后，通过最大化基于多重统计相似度量的代价函数，得到所提出的MORKF。MORKF保证了迭代在弱约束下的收敛性，且本文从理论上分析了近似误差的有界性。给出了相似函数的选择策略，并与现有鲁棒方法进行比较。仿真结果验证了该滤波器的优越性。

关键词组：卡尔曼滤波；多重统计相似度量；多样野值；定点迭代；状态估计

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Reference

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一种新型多样野值鲁棒卡尔曼滤波器

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