抗差自适应UKF算法在地基光学跟踪空间目标中的应用

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

系统工程与电子技术 ›› 2018, Vol. 40 ›› Issue (3): 623-629.doi: 10.3969/j.issn.1001-506X.2018.03.21

抗差自适应UKF算法在地基光学跟踪空间目标中的应用

刘光明, 徐帆江

中国科学院软件研究所天基综合信息系统重点实验室, 北京 100190

出版日期:2018-02-26 发布日期:2018-02-24

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

LIU Guangming, XU Fanjiang

Science and Technology on Integrated Information System Laboratory, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China

Online:2018-02-26 Published:2018-02-24

摘要/Abstract

摘要：

滤波过程中若噪声的统计特性发生时变,则会引起传统无迹卡尔曼滤波(unscented Kalman filter,UKF)的滤波精度快速降低、滤波收敛性不定甚至发散,针对这个问题提出了具有鲁棒性的UKF算法。首先根据极大后验估计(maximum a posterior estimate,MAPE)原理,推导出无偏的近似最优MAPE常值噪声统计特性的滤波估计公式,并给出了时变噪声统计估计器相关参数的一整套递推公式。考虑到观测数据粗差的存在,将可以在线估计时变噪声特性的方法和具有鲁棒特性的滤波因子相结合,以有效抑制观测数据的粗差值对滤波稳定性和收敛性的影响。最后,以地面站对空间非合作目标的光学测角跟踪为应用背景的仿真实例表明，该算法在噪声统计特性未知或不准确且过程噪声矩阵时变、观测数据存在个别粗差情况下,滤波依然收敛,其滤波精度及稳定性提高较为明显。

Abstract:

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.

刘光明, 徐帆江. 抗差自适应UKF算法在地基光学跟踪空间目标中的应用[J]. 系统工程与电子技术, 2018, 40(3): 623-629.

LIU Guangming, XU Fanjiang. Application of robustly adaptive UKF algorithm in ground-based bearings-only tracking for space targets[J]. Systems Engineering and Electronics, 2018, 40(3): 623-629.

抗差自适应UKF算法在地基光学跟踪空间目标中的应用

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

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