A Multi-Mode PDR Perception and Positioning System Assisted by Map Matching and Particle Filtering

Abstract

Currently, pedestrian dead reckoning (PDR) is widely used in indoor positioning. Since there are restrictions on a device’s 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 user’s 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 map-aided 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 step-wise method. The localization error can reduce to approximately 2.2 m when the smartphone is held at certain orientations.

A Multi-Floor Indoor Pedestrian Localization Method Using Landmarks Detection for Different Holding Styles

“…Moreover, they tested their system in only one scenario and with only one holding style. e works in ( [9,11,13,31] reported good results with the accuracy that can reach 1 m using the complex filters. As a matter of course, complex filters require a lot of computational costs and power…”

“…e other studies were developed by applying the EKF, UKF, and PF methods. Note that as the table shows, recent studies [13,31], including the proposed method, provide a method to handle various holding styles. e system in [12] achieved a positioning accuracy of 1.5 m; however, their experimental setup is simple with a straight line and an L-shape path.…”

Multi-Floor Indoor Pedestrian Dead Reckoning with a Backtracking Particle Filter and Viterbi-Based Floor Number Detection

“…This is similar to using graphs [ 42 ], but a mesh is also more memory efficient. Reference [ 22 ] used a Gaussian curve to weigh particles in addition to removing impossible particles. What makes map matching especially powerful is that it can be used when the initial position is unknown.…”

“…Large diversity means the PF can diverge heavily from the PDR output (and/or ignore physical constraints), causing multimodal state distributions. This multimodality problem is often mentioned [ 19 , 22 , 43 ], but mostly left unsolved. Reference [ 23 ] proposed an approximation of a Gaussian Kernel Density Estimator (KDE) to select the most probable mode of the multimodal particle distribution instead of averaging all particles.…”

“…The output of a PDR algorithm is often matched to a scaled floor plan by a Particle Filter (PF) [ 20 , 21 , 22 , 23 , 24 ]. Wall locations are used as physical constraints, eliminating drift caused by both heading and step length estimation errors.…”