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首页> 外文期刊>Mechatronics, IEEE/ASME Transactions on >A Particle Filter-Based Matching Algorithm With Gravity Sample Vector for Underwater Gravity Aided Navigation
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A Particle Filter-Based Matching Algorithm With Gravity Sample Vector for Underwater Gravity Aided Navigation

机译：基于粒子滤波和重力样本矢量的水下重力辅助匹配算法

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摘要

Gravity matching algorithm is a key technique of gravity aided navigation for underwater vehicles. The reliability of traditional single point matching algorithm can be easily affected by environmental disturbance, which results in mismatching and decrease of navigation accuracy. Therefore, a particle filter (PF)-based matching algorithm with gravity sample vector is proposed. The correlation between adjacent sample points of inertial navigation system is considered in the vector matching algorithm in order to solve the mismatching problem. The current sampling point matching result is rectified by the vectors composed by the selected sampling points and matching point. The amount of selected sampling points is determined by the gravity field distribution and the real-time performance of the algorithm. A PF-based on Bayesian estimation is introduced in the proposed method to overcome the divergence disadvantage of the traditional point matching algorithm in some matching areas with obvious gravity variation. Simulation results prove that compared with the traditional methods, the proposed method is robust to the changes of gravity anomaly in the matching areas, with more accurate and reliable matching results.
机译：重力匹配算法是水下航行器重力辅助导航的关键技术。传统的单点匹配算法的可靠性很容易受到环境干扰的影响，导致匹配不正确，导航精度下降。因此，提出了一种基于粒子滤波的重力样本矢量匹配算法。为了解决不匹配问题，在矢量匹配算法中考虑了惯性导航系统相邻采样点之间的相关性。当前的采样点匹配结果由所选采样点和匹配点组成的向量进行校正。所选采样点的数量取决于重力场分布和算法的实时性能。提出了一种基于贝叶斯估计的PF算法，克服了传统点匹配算法在一些重力变化明显的匹配区域的发散性缺点。仿真结果表明，与传统方法相比，该方法对匹配区域重力异常的变化具有较强的鲁棒性，匹配结果更加准确可靠。

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