A Novel Nonlinear Stochastic-Resonance-Enhanced Acceleration Data Processing Approach

Abstract

Smartphones are indispensable tools in modern social life, and they can be used for online shopping, electronic payment, gaming, and navigation. In particular, low-cost inertial measurement unit (IMU) sensors are widely integrated into smartphones, so pedestrian dead reckoning (PDR) positioning techniques based on smartphone IMU sensors have been applied and developed. PDR positioning techniques require acceleration data for step detection, step length estimation, and step heading estimation. However, due to the cost limitations of the built-in IMU sensor in smartphones, acceleration data contains measurement noise and interference, resulting in poor consistency in acceleration peak detection and the generation of false peaks, which is not conducive to step detection and accurate step length estimation. Therefore, this paper proposes a stochastic resonance (SR) enhancement method for smartphone IMU acceleration data. The SR-enhanced acceleration data has better peak consistency and is conducive to step detection. Finally, the algorithm is evaluated using actual measurement data collected from a smartphone. The results show that the SR-enhanced acceleration data has excellent peak consistency and higher step detection accuracy.