Enhanced heuristic drift elimination with adaptive zerovelocity detection and heading correction algorithms for pedestrian navigation As pedestrian deadreckoning (PDR), based on footmounted inertial sensors, suffers from accumulated error in velocity and heading, an improved heuristic drift elimination (iHDE) with a zerovelocity update (ZUPT) algorithm was proposed for simultaneously reducing the error in heading and velocity in complex paths, i.e., with pathways oriented at 45xc2xb0, curved corridors, and wide areas. However, the iHDE algorithm does not consider the changes in pedestrian movement modes, and it can deteriorate when a pedestrian walks along a straight path without a predefined dominant direction. To solve these two problems, we propose enhanced heuristic drift elimination (eHDE) with an adaptive zerovelocity update (AZUPT) algorithm and novel heading correction algorithm. The relationships between the magnitude peaks of the yaxis angular rate and the detection thresholds were established only using the readings of the threeaxis accelerometer and the threeaxis gyroscopic, and a mechanism for constructing temporary dominant directions in real time was introduced. Real experiments were performed and the results showed that the proposed algorithm can improve the stillphase detection accuracy of a pedestrian at different movement motions and outperforms the iHDE algorithm in complex paths with many straight features.

A MultiMode PDR Perception and Positioning System Assisted by Map Matching and Particle Filtering Currently, pedestrian dead reckoning (PDR) is widely used in indoor positioning. Since there are restrictions on a devicexe2x80x99s pose in the procedure of using a smartphone to perform the PDR algorithm, this study proposes a novel heading estimation solution by calculating the integral of acceleration along the direction of the userxe2x80x99s movement. First, a lightweight algorithm, that is, a finite state machine (FSM)decision tree (DT), is used to monitor and recognize the device mode, and the characteristics of the gyroscope at the corners are used to improve the heading estimate performance during the linear phase. Moreover, to solve the problem of heading angle deviation accumulation on positioning, a mapaided particle filter (PF) and behavior perception techniques are introduced to constrain the heading and correct the trajectory through the wall after filtering. The results indicate that the recognition of phone pose can be 93.25%. The improved heading estimation method can achieve higher stability and accuracy than the traditional stepwise method. The localization error can reduce to approximately 2.2 m when the smartphone is held at certain orientations.