| dc.description.abstract | The rise of Global Navigation Satellite Systems (GNSS) has made navigation algorithms mainstream. However, when Global Positioning System (GPS) signals are disrupted or fail, GNSS-dependent navigation systems also become ineffective. As a result, autonomous navigation technologies that do not rely on GNSS have gained increasing attention. This study aims to develop a pedestrian positioning system based on inertial navigation theory, utilizing attitude estimation algorithms such as the Madgwick filter and the SAAM (a fast attitude estimation algorithm based on accelerometers and magnetometers). By integrating the Zero Velocity Update (ZUPT) constraint method and altitude information, an Error-State Kalman Filter (ESKF) is constructed to enhance the pedestrian positioning system.
To verify the positioning performance of the algorithm, a simulated two-dimensional inertial signal was first established. Using sine waves and simulated pedestrian gait signals, a rectangular path was created, and the positioning accuracy of inertial signals processed by the ESKF algorithm was compared with unfiltered signals. The accuracy was assessed using indicators such as Error Percentage (EP) and Normalized-Path Root Mean Square Error (NP-RMSE). The results showed that EP decreased from 16.4% to 2.96%, while NP-RMSE reduced from 12.5% to 1.89%, demonstrating that the ESKF algorithm effectively filters noise and improves inertial signal positioning accuracy.
In practical experiments, the system was tested on linear, rectangular, and circular paths, integrating a wireless positioning measurement system. The positioning accuracy of different attitude estimation algorithms was evaluated, revealing that the Madgwick algorithm achieved EP and NP-RMSE values of 1.86%, 3.1%, and 2.7% and 7.63%, 6.5%, and 2.7%, respectively. Meanwhile, the SAAM algorithm yielded values of 11.79%, 3.8%, and 7.1% for EP and 15%, 9.4%, and 4.5% for NP-RMSE. The comparison indicates that the Madgwick algorithm provides better attitude accuracy. However, regardless of the attitude estimation algorithm used, the trajectory results underestimated actual displacement, likely due to insufficient accuracy in ZUPT state recognition.
Additionally, the altitude RMSE values for both systems were 0.42m, 0.21m, and 0.8m for the Madgwick algorithm and 0.42m, 0.21m, and 0.85m for the SAAM algorithm, demonstrating precise altitude positioning accuracy. The computation times were 4.85s, 4.26s, and 62.84s for the Madgwick algorithm and 4.05s, 3.65s, and 60.71s for the SAAM algorithm. Although the difference decreased with increasing computational load, the SAAM algorithm exhibited higher computational efficiency, making it a promising candidate for real-time positioning applications.
The experimental results indicate that the proposed system has successfully established a preliminary pedestrian positioning prototype. However, to achieve real-time positioning, the ZUPT and attitude estimation algorithms should be adapted with optimized parameters for different pedestrian states to enhance positioning accuracy and ultimately realize full navigation functionality. | en_US |