Abstract
The development of smartphone Micro-Electro-Mechanical Systems (MEMS) inertial sensors has provided opportunities to improve indoor navigation and positioning for location-based services. One area of indoor navigation research uses pedestrian dead reckoning (PDR) technology, in which the mobile phone must typically be held to the pedestrian’s chest. In this paper, we consider navigation in three other mobile phone carrying modes: “calling,” “pocket,” and “swinging.” For the calling mode, in which the pedestrian holds the phone to their face, the rotation matrix method is used to convert the phone’s gyroscope data from the calling state to the holding state, allowing calculation of the stable pedestrian forward direction. For a phone carried in a pedestrian’s trouser pocket, a heading complementary equation is established based on principal component analysis and rotation approach methods. In this case, the pedestrian heading is calculated by determining a subset of data that avoid 180° directional ambiguity and improve the heading accuracy. For the swinging mode, a heading capture method is used to obtain the heading of the lowest point of the pedestrian’s arm swing as they hold the phone. The direction of travel is then determined by successively adding the heading offsets each time the arm droops. Experimental analysis shows that 95% of the heading errors of the above three methods are less than 5.81°, 10.73°, and 9.22°, respectively. These results present better heading accuracy and reliability.
Highlights
We consider pedestrian heading estimation methods for three phone carrying modes. e rotation matrix method is used in the calling state, considering the problem of nonhorizontal head rotations, and making improvements to the accuracy and stability of the heading estimates. e complementary PCA and RA methods are adopted for the pocket state, avoiding the 180° directional ambiguity and reducing the impacts of cumulative errors
Due to its good stability and the more mature heading estimation method, the holding mode offers the most accurate and reliable pedestrian heading estimates. erefore, we focus on pedestrian heading estimation methods for the other three mobile phone carrying modes
A is the phone’s three-axis accelerometer data, Δβ is a fixed horizontal rotation angle, calculated from the heading before and after the carrying mode change, Δφ is the relative heading change caused by the nonhorizontal rotation of the head in the state of making a call, which is detected by the changes in the pitch and roll angles, and g is the local acceleration due to gravity. e corrected gyroscope data are obtained through the rotation matrix method, which effectively avoids the errors caused by the nonhorizontal shaking of the mobile phone in the calling state
Summary
Used mobile phone carrying modes include holding, calling, pocket, and swinging. A is the phone’s three-axis accelerometer data, Δβ is a fixed horizontal rotation angle, calculated from the heading before and after the carrying mode change, Δφ is the relative heading change caused by the nonhorizontal rotation of the head in the state of making a call, which is detected by the changes in the pitch and roll angles, and g is the local acceleration due to gravity. E corrected gyroscope data are obtained through the rotation matrix method, which effectively avoids the errors caused by the nonhorizontal shaking of the mobile phone in the calling state. When a pedestrian followed a sequence of instructions (“go straight,” “turn right,” and “turn left”), the gyroscope z-axis data obtained by the rotation matrix method in the calling state were approximately.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
