As a new navigation method, pedestrian dead reckoning (PDR) has attracted much attention because it is less susceptible to environmental factors. To solve the indoor PDR problem, data of a micro-electro-mechanical system-inertial measurement unit (MEMS-IMU) are collected and analyzed. A step detection algorithm is developed for motion classification and interval symmetry, and step length estimation model is established for step frequency adjustment. Thus a MEMS-IMU indoor PDR based on the motion classification and step frequency adjustment is constructed to realize accurate positioning. For different individuals, personalized step estimation model parameters are used to improve the positioning performance. Experimental results show that, the indoor PDR based on motion classification and step frequency adjustment reduces positioning error by 32.6% as compared to a traditional method using peak detection and a nonlinear model, achieving high positioning accuracy without resorting to any other positioning techniques.
LI Ruo-han1, ZHANG Jin-yi1,2,3, XU De-zheng2, CHEN Xing-xiu1, XU Qin-le2
. Micro-Electro-Mechanical System-Inertial Measurement Unit Indoor Pedestrian Dead Reckoning Based on Motion Classification and Step Frequency Adjustment[J]. Journal of Shanghai University, 2014
, 20(5)
: 612
-623
.
DOI: 10.3969/j.issn.1007-2861.2014.01.018
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