Application of robustly adaptive UKF algorithm in ground-based bearings-only tracking for space targets

被引：1

作者：

Liu G. ^{[1
]}

Xu F. ^{[1
]}

机构：

[1] Science and Technology on Integrated Information System Laboratory, Institute of Software, Chinese Academy of Sciences, Beijing

来源：

Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2018年 / 40卷 / 03期

关键词：

Adaptive unscented Kalman filter (UKF) algorithm; Filter stability; Robust filter; Time-varying noise statistic estimator;

D O I：

10.3969/j.issn.1001-506X.2018.03.21

中图分类号：

O212 [数理统计];

学科分类号：

摘要：

In the statistical characteristics of the noise filtering process, time-varing will cause the filtering precision decreasing fast, indefinite filtering convergence or even divergence of the traditional unscented Kalman filter (UKF). To deal with that the robust UKF algorithm is proposed. According to the maximum a posteriori estimate (MAPE) principle, the optimal approximate partial MAPE constant statistical characteristics of noise filtering estimation formulas are deduced, and a set of time-varying noise statistics estimator parameters recursive formulas are given. Considering coarse difference existing in observation data, noise characteristics of online estimation and robust filtering factors are combined in order to effectively suppress coarse difference observation datas influence on the stability and convergence of the filter. Simulation examples on the ground-based bearings-only tracking for the non-cooperative space target show that the proposed adaptive UKF algorithm still converges under the condition of unknown and time-varying noise statistic and coarse difference existing in observation data, with greatly improved filtering stability. © 2018, Editorial Office of Systems Engineering and Electronics. All right reserved.

引用

页码：623 / 629

页数：6

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