Document Type : Original Research Paper
Authors
1 GIS Division, School of Surveying and Geospatial Engineering, University of Tehran, Tehran, Iran
2 Faculty of Mining Engineering, Sahand University of Technology, Tabriz, Iran
Abstract
Background and Objectives: Positioning in indoor spaces and environments without Global Navigation Satellite System signals is one of the major challenges in the development of location‑based services in smart cities. Among various approaches, infrastructure‑independent methods such as smartphone‑based Pedestrian Dead Reckoning are considered a reliable solution, especially in critical conditions and when infrastructures are damaged or destroyed. This research aims to present an innovative approach by providing a two‑dimensional model for simulating and analyzing specific human movement activities in critical environments, including normal walking, crawling on hands and knees, and belly crawling.
Methods: The proposed process consists of data collection, signal segmentation, gait cycle detection, step counting, movement type recognition, step length estimation, direction determination, and internal coordinate calculation. Data was collected in two phases: training and testing. The signals were segmented using the Pruned Exact Linear Time algorithm and the Functional Pruning Optimal Partitioning algorithm. Subsequently, movement type was recognized by employing Dynamic Time Warping, and step length was estimated using the Weinberg model.
Findings: In the proposed approach, the accuracy of path estimation under different conditions reached an average of 0.17 percent. Furthermore, the direction of movement was determined by fusing data from the accelerometer, gyroscope, and magnetometer using the Gradient Descent Algorithm. The results showed that in the primary Pedestrian Dead Reckoning method, for a path with an average length of 238.226 meters, the mean error was approximately 2.97 ± 1.04 meters. Subsequently, the use of a particle filter based on map matching reduced the error to 0.35 ± 0.07 meters.
Conclusion: The results of this study indicate that designing an intelligent and self-adaptive processing chain for analyzing motion signals can significantly enhance the accuracy and robustness of the Pedestrian Dead Reckoning method in indoor environments and critical conditions. Furthermore, the findings confirm that the smartphone sensor‑based Pedestrian Dead Reckoning method can provide accurate and reliable positioning even when the type of movement or the way the device is carried changes.
Keywords
- Indoor Positioning
- Pedestrian Dead Reckoning (PDR)
- Map Matching
- Smartphone Sensors
- Critical Conditions
Main Subjects
COPYRIGHTS
© 2026 The Author(s). This is an open-access article distributed under the terms and conditions of the Creative Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
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