Minimum dispersion coefficient criteria based positioning algorithm for BDS
Minimum dispersion coefficient criteria based positioning algorithm for BDS
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DOI：
10.24425/aee.2018.124737
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发表时间：
2023-04
影响因子：
1.3
通讯作者：
Lina Wang;Linlin Li
中科院分区：
文献类型：
--
作者：
Lina Wang;Linlin Li
北斗导航卫星系统是全球四大导航卫星系统之一。北斗系统的定位算法受到了广泛的关注。在研究卡尔曼滤波（KF）算法的基础上，提出了一种新的北斗定位算法--最小色散系数卡尔曼滤波（MDCCKF）定位算法。MDCCKF算法采用最小分散系数准则（MDCC），利用α稳定分布（ASD）模型有效地描述非高斯噪声，特别是定位中的脉冲噪声，去除噪声的影响。MDCCKF通过最小化定位误差的分散系数，保证了在高斯和非高斯环境下的定位精度。与原始KF算法相比，MDCCKF算法具有更高的定位精度和鲁棒性。MDCCKF算法为未来的研究提供了有见地的结果。
: The BeiDou navigation satellite system (BDS) is one of the four global navigation satellite systems. More attention has been paid to the positioning algorithm of the BDS. Based on the study on the Kalman filter (KF) algorithm, this paper proposed a novel algorithm for the BDS, named as the minimum dispersion coefficient criteria Kalman filter (MDCCKF) positioning algorithm. The MDCCKF algorithm adopts minimum dispersion coefficient criteria (MDCC) to remove the influence of noise with an alpha-stable distribution (ASD) model which can describe non-Gaussian noise effectively, especially for the pulse noise in positioning. By minimizing the dispersion coefficient of the positioning error, the MDCCKF assures positioning accuracy under both Gaussian and non-Gaussian environment. Compared with the original KF algorithm, it is shown that the MDCCKF algorithm has higher positioning accuracy and robustness. The MDCCKF algorithm provides insightful results for potential future research.