PDR-WiFi Fusion Positioning Based on CNN Denoising

This paper addresses the issues of significant noise and noticeable bias in the signals of low-cost inertial sensor integrated into smartphones. It proposes a method that combines Convolutional Neural Network (CNN) denoising techniques with Pedestrian Dead Reckoning (PDR) to enhance the accuracy of gait estimation and step length detection. To further resolve the problem of error accumulation in long-term PDR usage, an Extended Kalman Filter (EKF) is introduced to fuse the denoised PDR results with WiFi positioning. This approach effectively mitigates the error accumulation in PDR and reduces the instability of WiFi positioning, providing more accurate localization services in complex indoor environments. After applying the CNN denoising technique, the PDR positioning accuracy improved from 2.28 meters to 1.70 meters, a 25.44% improvement compared to the original accuracy. The positioning accuracy of the PDR and WiFi fusion method improved from 1.13 meters to 1.02 meters, representing a 9.73% improvement.