| 摘要: |
| 首先针对个人航位推算系统中计步算法的阈值设定问题,提出了一种基于有限状态机的阈值自学习算法.通过该算法可以在较短的时间内(10s)获得自适应阈值,提高计步算法的准确性.然后在利用该阈值进行计步的同时,对连续步态进行分割,并以分割结果作为动态窗口进行主成分分析,可以获取目标运动的方向.此外,通过利用运动步态模型中加速度变化的规律可以有效解决180°模糊问题.实验分析结果表明,相比于固定窗口的 PCA 分析方法,基于动态窗口的 PCA 在数据处理量降低61.2%的情况下,其准确度提高了11.1%. |
| 关键词: 阈值自学习 计步算法 主成分分析 180°模糊 方向估计 |
| DOI： |
| 分类号: |
| 基金项目:国家自然科学基金(61170121) |
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| Dynamic-Window PCA Algorithm for Step Direction Estimation |
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ZHU Xiang-Jun, CHEN Jing, LIANG Jiu-Zhen
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School of Internet of Thing Engineering, Jiangnan University, Wuxi 214122, China
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| Abstract: |
| Firstly, to solve the problem of threshold setting for step-counting in personal dead reckoning system, this paper proposes a self-learning of threshold based on FSM. It can obtain an adaptive threshold in a short period of time(about 10s), and improve the accuracy of pedometer algorithm. Secondly, the FSM splits the continuous gait into individuals, which are processed by PCA to obtain the step direction. In addition, the algorithm effectively solves the 180° ambiguity by analyzing the variation of forward acceleration. The results of the experiment show that the dynamic-window PCA decreases the amount of data to be processed by 61.2% and increases the accuracy by 11.1% compared to the fixed-window PCA. |
| Key words: self-learning of threshold step-counting PCA 180°ambiguity step direction estimation |
