Simulated-Zero Velocity Update Method for smartphone Navigation

An inertial sensor-based pedestrian navigation system, especially a navigation system using a Pedestrian Dead Reckoning (PDR) algorithm, is usually navigated with a Zero Velocity Update (ZUPT) algorithm to improve navigation and positioning Accuracy. The rich navigation sensor in smartphone makes it possible to use smartphone as the medium of pedestrian navigation. In fact, the self-contained low-cost inertial sensor of smartphone often needs such as ZUPT algorithms to suppress the rapid divergence of error, however, most traditional ZUPT algorithms putting the sensors on the foot, which make it impossible to detect a clear zero-speed moment for smartphone because we usually use smartphone with hand not put it on the foot and the traditional ZUPT algorithm can’t work. In this paper a Simulate-Zero Velocity Update (S-ZUPT) algorithm is proposed. The S-ZUPT algorithm detects the pedestrian walking steps through the accelerometer information collected by the smartphone, and carries out the Kalman Filter algorithm between the two steps moments to restrain the heading divergence caused by the low-precision sensors from the smartphone and improve navigation accuracy. Experiments show that the using S-ZUPT algorithm can effectively restrain the divergence of heading, reduce the error between the navigation route and the actual route, improving navigation accuracy.

[1]  Fernando Seco Granja,et al.  Indoor pedestrian navigation using an INS/EKF framework for yaw drift reduction and a foot-mounted IMU , 2010, 2010 7th Workshop on Positioning, Navigation and Communication.

[2]  Jianye Liu,et al.  Improved GNSS-based indoor positioning algorithm for mobile devices , 2017, GPS Solutions.

[3]  Naser El-Sheimy,et al.  An Integrated PDR/GNSS Pedestrian Navigation System , 2015 .

[4]  Jaehyun Park,et al.  Waist mounted Pedestrian Dead-Reckoning system , 2012, 2012 9th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI).

[5]  Valérie Renaudin,et al.  Magnetic field based heading estimation for pedestrian navigation environments , 2011, 2011 International Conference on Indoor Positioning and Indoor Navigation.

[6]  Henk L. Muller,et al.  Personal position measurement using dead reckoning , 2003, Seventh IEEE International Symposium on Wearable Computers, 2003. Proceedings..

[7]  Zhang Rong,et al.  Online three-axis magnetometer calibration for a pedestrian navigation system using a foot-mounted inertial navigation system , 2016 .

[8]  Liang Chen,et al.  A two-dimensional pedestrian navigation solution aided with a visual gyroscope and a visual odometer , 2013, GPS Solutions.

[9]  Eric Foxlin,et al.  Pedestrian tracking with shoe-mounted inertial sensors , 2005, IEEE Computer Graphics and Applications.

[10]  Qinghua Zeng,et al.  Seamless Pedestrian Navigation Methodology Optimized for Indoor/Outdoor Detection , 2018, IEEE Sensors Journal.