An Optimized Pedestrian Inertial Navigation Method Based on the Birkhoff Pseudospectral Method

Area of Science:

• Robotics
• Navigation Systems
• Signal Processing

Background:

• Pedestrian inertial navigation is crucial for seamless indoor/outdoor positioning.
• Extended Kalman Filter (EKF) methods suffer from cumulative errors due to inertial measurement unit (IMU) noise.
• IMU noise degrades trajectory estimation accuracy over extended periods.

Purpose of the Study:

• To propose a post-processing trajectory optimization approach for pedestrian inertial navigation.
• To address the limitations of traditional EKF-based methods in handling IMU noise.
• To enhance the accuracy and robustness of pedestrian trajectory estimation.

Main Methods:

• Utilized the Birkhoff pseudospectral method for trajectory optimization.
• Employed Zero-Velocity Update (ZUPT) and EKF for initial attitude and position estimates.
• Parameterized continuous-time acceleration measurements using Chebyshev nodes for noise suppression.

Main Results:

• The Birkhoff pseudospectral method effectively suppresses noise interference from IMU accelerometers.
• Simulation experiments confirmed the algorithm's superior noise suppression capabilities.
• Physical experiments showed a reduction in final position error by approximately 90% compared to traditional EKF methods.

Conclusions:

• The proposed Birkhoff pseudospectral method offers a significant improvement in pedestrian inertial navigation accuracy.
• This approach effectively mitigates cumulative errors caused by IMU noise.
• The method provides a robust solution for long-duration, high-accuracy pedestrian trajectory estimation.