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.

An improved pedestrian ttracking method based on wifi fingerprinting and pedestrian dead reckoning WiFi based positioning has great potential for use in indoor environments because WiFi signals are nearubiquitous in many indoor environments. With a Reference Fingerprint Map (RFM), fingerprint matching can be adopted for positioning. Much assisting information can be adopted for increasing the accuracy of WiFi based positioning. One of the most adopted pieces of assisting information is the Pedestrian Dead Reckoning (PDR) information derived from inertial measurements. This is widely adopted because the inertial measurements can be acquired through a Commercial Off The Shelf (COTS) smartphone. To integrate the information of WiFi fingerprinting and PDR information, many methods have adopted filters, such as Kalman filters and particle filters. A new methodology for integration of WiFi fingerprinting and PDR is proposed using graph optimization in this paper. For the WiFi based fingerprinting part, our method adopts the stateofart hierarchical structure and the Penalized Logarithmic Gaussian Distance (PLGD) metric. In the integration part, a simple extended Kalman filter (EKF) is first used for integration of WiFi fingerprinting and PDR results. Then, the tracking results are adopted as initial values for the optimization block, where WiFi fingerprinting and PDR results are adopted to form an concentrated cost function (CCF). The CCF can be minimized with the aim of finding the optimal poses of the user with better tracking results. With both realscenario experiments and simulations, we show that the proposed method performs better than classical Kalman filter based and particle filter based methods with both less average and maximum positioning error. Additionally, the proposed method is more robust to outliers in both WiFi based and PDR based results, which is commonly seen in practical situations.