Performance analysis of improved iterated cubature Kalman filter and its application to GNSS/INS
Performance analysis of improved iterated cubature Kalman filter and its application to GNSS/INS
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改进迭代体积卡尔曼滤波器性能分析及其在GNSS/INS中的应用
DOI：
10.1016/j.isatra.2016.09.010
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发表时间：
2017-01-01
期刊：
影响因子：
7.3
通讯作者：
Liu, Xiao
中科院分区：
文献类型：
--
作者：
Cui, Bingbo;Chen, Xiyuan;Liu, Xiao
为了提高GNSS/INS组合导航系统的精度和鲁棒性，考虑状态相关噪声和系统不确定性，提出了一种改进的迭代容积卡尔曼滤波（IICKF）算法。首先，利用阻尼牛顿-拉夫逊算法和在线噪声估计器，推导出迭代高斯滤波器的简化框架。然后从理论上分析了迭代更新过程中状态相关噪声的影响，并采用一种增广形式的CKF算法来提高估计精度。通过外场试验和数值仿真验证了IICKF的性能，结果表明，与非迭代滤波相比，迭代滤波对系统不确定性的敏感性降低，与传统迭代滤波相比，IICKF对偏航、滚转和俯仰的精度分别提高了48.9%、73.1%和83.3%。(C)2016年伊萨。由爱思唯尔有限公司出版。保留所有权利。
In order to improve the accuracy and robustness of GNSS/INS navigation system, an improved iterated cubature Kalman filter (IICKF) is proposed by considering the state-dependent noise and system uncertainty. First, a simplified framework of iterated Gaussian filter is derived by using damped Newton-Raphson algorithm and online noise estimator. Then the effect of state-dependent noise coming from iterated update is analyzed theoretically, and an augmented form of CKF algorithm is applied to improve the estimation accuracy. The performance of IICKF is verified by field test and numerical simulation, and results reveal that, compared with non-iterated filter, iterated filter is less sensitive to the system uncertainty, and IICKF improves the accuracy of yaw, roll and pitch by 48.9%, 73.1% and 83.3%, respectively, compared with traditional iterated KF. (C) 2016 ISA. Published by Elsevier Ltd. All rights reserved.