引用本文: 毕京学, 甄杰, 姚国标, 桑文刚, 宁一鹏, 郭秋英. 面向智能手机的改进有限状态机步态探测算法[J]. 武汉大学学报 ( 信息科学版).
BI Jingxue, ZHEN Jie, YAO Guobiao, SANG Wengang, NING Yipeng, GUO Qiuying. Improved Finite State Machine Step Detection Algorithm for Smartphone[J]. Geomatics and Information Science of Wuhan University.
 Citation: BI Jingxue, ZHEN Jie, YAO Guobiao, SANG Wengang, NING Yipeng, GUO Qiuying. Improved Finite State Machine Step Detection Algorithm for Smartphone[J]. Geomatics and Information Science of Wuhan University.

## Improved Finite State Machine Step Detection Algorithm for Smartphone

• 摘要: 针对室内定位行人航位推算中步态探测算法步数识别准确率不够高、同步控制不够精确以及位置估计存在较大偏差等问题，提出了一种面向智能手机平端活动的改进有限状态机步态探测算法。通过设定有限状态对应步行过程合加速度变化趋势，利用相邻合加速度差值和上/下坡次数阈值实现步数识别和步态周期估计。在211米走廊内由2名实验人员分别平端智能手机开展实验，结果表明：改进算法步数识别准确率为100%，每一步平均识别时间提前了0.004秒，平均位置误差为0.384米，相比于自相关分析和加速度差分有限状态机算法，识别准确率、同步控制精度和位置估计精度分别至少提高了0.7%、60%和21.15%。本文所提算法在步数识别、同步控制以及位置估计方面优于现有算法。

Abstract: To solve the problems in pedestrian dead reckoning algorithms for indoor positioning, of which the step recognition accuracy for step detection is not high enough, the synchronous control is not precise enough, and there is a large location deviation. An algorithm of improved finite state machine step detection for the activity of flat holding smartphone was proposed. A finite number of states were set to correspond to the trend of resultant acceleration variation during walking. Step detection and step cycle estimation were realized based on adjacent resultant acceleration difference and several thresholds of climbing and descending times. Experimental tests were conducted by two testers in 211 meters corridors with flat holding smartphone, respectively. Experimental results show that the accuracy of two step detection tests are both 100% by using the improved algorithm. It is 0.004 seconds earlier on average than the actual time for each step. And the average location error is 0.384m. Compared to the auto-correction analysis and acceleration differential based on finite state machine algorithms, the accuracy of step recognition, synchronous control and location estimation are improved at least 0.7%, 60% and 21.15%, respectively. The proposed algorithm behaves better than the existing algorithms in the aspects of the step recognition, the synchronous control and location estimation.

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