Abstract—The demand for navigating a user with a hand-held device, especially in Global Position System (GPS) denied environments, has tremendously increased over the last few years. Accelerometers, gyroscopes, and magnetometers are the most commonly found sensors in the smartphones that provide Three Dimensional (3D) acceleration and attitude of the phone. Algorithm of pedestrian navigation with smart phones modes switching is studied. When the sensor is rigidly mounted on the user’s body, the trajectory of the user can easily be reconstructed. The placement of the phone can vary overtime as a user performs different tasks. When the sensor’s location is dynamically changing, the situation becomes much more complex. Smartphone modes among three most commonly used are considered in this research, texting mode, ear-talking mode and waist mode. Using the machine learning method of decision trees is developed to recognize smartphones’ modes. The average accuracy of the selected classifier is > 92.8%. According to the detected smartphone mode, adaptive heading angle compensation algorithms are applied, the location error in the horizontal direction from the starting point to the ending point is approximately less than 30 meters when people with smartphone mode switched walk a distance of 1000 meters, and the feasibility of the algorithm is verified. The dynamic measurement precision of pedestrian navigation using a smart phone is improved, and it is more accurate to use a smart phone to realize pedestrian navigation in different smartphone modes.

Index Terms—Phone inertial sensors, pedestrian navigation,smartphone mode,decision trees

Cite: Yu Liu, Yanping Chen, Mu Zhou, Yibing Wang, Shuang Gong, and Xin Zhang, “Multi-Mode Dynamically Switching Pedestrian Navigation Using Smart Phone Inertial Sensors," Journal of Communications, vol. 10, no. 12, pp.955-962, 2015. Doi: 10.12720/jcm.10.12.955-962