Enhanced Heuristic Drift Elimination with Adaptive Zero-Velocity Detection and Heading Correction Algorithms for...

Area of Science:

• * Navigation and Positioning Systems
• * Inertial Sensor Data Processing
• * Human Motion Analysis

Background:

• * Pedestrian dead-reckoning (PDR) using foot-mounted inertial sensors accumulates errors in velocity and heading.
• * Existing improved heuristic drift elimination (iHDE) algorithms struggle with changing pedestrian movement modes and straight paths without a dominant direction.

Purpose of the Study:

• * To propose an enhanced heuristic drift elimination (eHDE) algorithm with adaptive zero-velocity update (AZUPT) and a novel heading correction algorithm.
• * To address the limitations of iHDE concerning pedestrian movement mode variations and straight-path navigation.

Main Methods:

• * Developed an adaptive zero-velocity update (AZUPT) algorithm to improve still-phase detection accuracy across different movements.
• * Introduced a novel heading correction mechanism using real-time temporary dominant direction construction.
• * Established relationships between angular rate peaks and detection thresholds using accelerometer and gyroscope data.

Main Results:

• * The proposed eHDE algorithm demonstrated improved still-phase detection accuracy for pedestrians in various motion states.
• * Experimental results confirmed that eHDE outperforms the iHDE algorithm, especially in complex paths with numerous straight segments.
• * The algorithm effectively handles changes in pedestrian movement modes and navigates straight paths without a pre-defined dominant direction.

Conclusions:

• * The enhanced heuristic drift elimination (eHDE) with adaptive zero-velocity update (AZUPT) offers a more robust solution for PDR.
• * This approach significantly reduces accumulated errors in velocity and heading for pedestrian navigation in diverse environments.
• * The proposed methods enhance the reliability and accuracy of inertial sensor-based PDR systems.