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基于改进K-means聚类的惯性行人导航零速检测算法

Chinese Journal of Sensors and Actuators(2022)

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Abstract
行人导航中不同运动状态下零速区间的运动数据也有所不同,这就要求零速检测算法具有良好的适应性.针对利用阈值实现零速检测的算法在多种运动状态下适应性差的问题,该文提出了一种基于改进K-means聚类的零速检测算法(zero-velocity interval detection algorithm based on improved K-means clustering,IKC).首先,在运动的开始阶段,通过K-means聚类对角速度数据进行聚类,从而得到零速区间与非零速区间的中心点;然后根据设定的数据点到中心点的距离条件对零速区间与非零速区间进行划分,相比于其他算法,优化了数据处理过程,有效缩短了计算时间,并且不依赖阈值条件,有效提高了该算法的适应性;同时,根据零速区间与非零速区间的持续时间判断运动状态是否改变,若发生改变则重新进行K-means聚类获取新运动状态的中心点.最后,在实际行人导航系统中对新提出的算法进行了实验验证,从计算量及行人导航精度等方面与步态特征提取的K均值聚类自适应判别算法(K-means clustering adaptive detection,KCA)、基于贝叶斯的自适应阈值零速检测算法(bayesian adaptive threshold detection,BAT)进行了对比分析.结果表明,本文提出的基于改进K-means聚类的零速检测算法不仅有效的减小了计算时间,而且具有较高的导航精度和导航稳定性.
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