A Handheld Inertial Pedestrian Navigation System With Accurate Step Modes and Device Poses Recognition

作者：Zhang, Hemin^*; Yuan, Weizheng; Shen, Qiang; Li, Tai; Chang, Honglong

来源：IEEE Sensors Journal, 2015, 15(3): 1421-1429.

DOI：10.1109/JSEN.2014.2363157

• Pedestrian navigation
• dead reckoning
• motion recognition
• machine learning
• microsensors

In this paper, a handheld inertial pedestrian navigation system (IPNS) based on low-cost microelectromechanical system sensors is presented. Using the machine learning method of support vector machine, a multiple classifier is developed to recognize human step modes and device poses. The accuracy of the selected classifier is >85%. A novel step detection model is created based on the results of the classifier to eliminate the over-counting and under-counting errors. The accuracy of the presented step detector is >98%. Based on the improvements of the step modes recognition and step detection, the IPNS realized precise tracking using the pedestrian dead reckoning algorithm. The largest location error of the IPNS prototype is similar to 40 m in an urban area with a 2100-m-long distance.

• 出版日期2015-3
• 单位西北工业大学