Smartphone-Based Attitude-Unconstrained Pedestrian Dead Reckoning System with Positioning Adjustment using Wi-Fi Fingerprinting

Mon, 15 Sep 2025 00:00:00 +0000

With people nowadays spending an increasing amount of time indoors, smartphone-based indoor positioning technology holds significant practical value. However, existing Pedestrian Dead Reckoning (PDR) algorithms typically require smartphones to maintain specific attitudes, severely limiting their practicality. Attitude changes affect both heading estimation accuracy and step detection performance, leading to positioning errors. Current research addressing attitude constraints primarily focuses on optimizing individual modules rather than providing comprehensive system solutions. This paper proposes a complete attitude-unconstrained smartphone PDR system integrated with Wi-Fi positioning technology. The system encompasses three core modules: (1) Step detection employing multi-sensor fusion technology with cross-sensor axis combinations; (2) Heading estimation adopting frequency-domain analysis to align the smartphone coordinate system with the actual walking direction through angle traversal and coordinate transformation; (3) Step length estimation using an enhanced Weinberg model based on biomechanical characteristics, comprehensively considering height, acceleration variations, and step frequency factors. The PDR results are subsequently adjusted using an adaptive weighted fusion mechanism integrating Wi-Fi fingerprinting. The complete proposed tracking solution is demonstrated through real-world experiments with average positioning errors of 0.66m and 1.1m, for pocket and reading modes respectively.

Lingming Yu | MSc in Electronics and Technology

Smartphone-Based Attitude-Unconstrained Pedestrian Dead Reckoning System with Positioning Adjustment using Wi-Fi Fingerprinting