Problem
Research questions and friction points this paper is trying to address.
Pedestrian Dead Reckoning
Attitude Estimation
AHRS
Quaternion Averaging
Indoor Navigation
Quaternion-Averaging-Based Adaptive Complementary Filter for Pedestrian Dead Reckoning With a Foot-Mounted AHRS
🤖 AI Summary
This work addresses the insufficient accuracy and efficiency of attitude estimation in foot-mounted AHRS-based pedestrian dead reckoning by proposing an adaptive complementary filtering method based on quaternion averaging. The approach fuses angular velocity, acceleration, and magnetic field measurements, employing Markley’s quaternion averaging instead of linear interpolation to achieve a more rigorous attitude fusion. Furthermore, it dynamically adjusts sensor weights according to gait phase detection and magnetic disturbance assessment. Compared to existing algorithms, the proposed method significantly reduces the root-mean-square error of attitude estimation while maintaining lower computational overhead than Kalman filters, thereby achieving an effective balance between precision and real-time performance.
📝 Abstract
Pedestrian Dead Reckoning (PDR) can be applied to indoor navigation systems. GPS suffers from signal degradation due to roofs and high-rise buildings, whereas PDR can estimate positions without being affected by such signal degradation. The accuracy of a foot-mounted AHRS(Attitude and Heading Reference System)-based PDR depends on the accuracy of the attitude estimation algorithm used in the AHRS. In this article, a Quaternion-Averaging-Based Adaptive Complementary Filter (QAACF) for PDR with a foot-mounted AHRS is proposed to improve estimation accuracy while reducing computational cost. QAACF fuses a quaternion derived from angular velocity with quaternions derived from acceleration and magnetic field measurements using Markley's quaternion averaging, which combines two quaternions more rigorously than linear interpolation. In addition, QAACF adaptively adjusts the weights of angular velocity, acceleration, and magnetic field measurements according to gait phases and the level of magnetic disturbances. Experimental results showed that the proposed QAACF achieves low Root Mean Square Errors (RMSEs) compared to existing attitude estimation filters while requiring lower computational cost than Kalman filters.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Quaternion Averaging
Adaptive Complementary Filter
Pedestrian Dead Reckoning
AHRS
Magnetic Disturbance Adaptation
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