Estimating Position Relation between Two Pedestrians Using Mobile Phones

Abstract

Abstract. In a complex indoor environment such as a huge station in an urban area, sometimes the direction and distance relative to another person are more important for pedestrians than their absolute positions, e.g. to search for a lost child. We define this information as the position relation. Our goal is to develop a position relation estimation method on a mobile phone with built-in motion sensors. In literature, methods of cooperative navigation using two pedestrians' positions estimated by pedestrian dead reckoning and a range sensor have been proposed. However, these methods cannot be applied to a mobile phone because pedestrian dead reckoning does not work well when a mobile phone is in a bag, and because there is no range sensor in a phone. In fact, no Bluetooth is reliable as a substitute range sensor. This paper proposes another approach to estimate the position relation of pedestrians. Our method finds the timing when two pedestrians are in close proximity to each other and walk together by using Bluetooth as a proximity sensor and corrects the parameters of position updates dynamically, even if absolute positions are unknown. The algorithm and evaluation results are presented in this paper.

A Particle Filter-Based Reinforcement Learning Approach for Reliable Wireless Indoor Positioning

“…As reported in previous studies [20], [22] , heading orientation θ t and stride length t are interfered by zero-mean Gaussian random noises ε and ε , respectively. Variables θ t and t are estimated by PDR methods.…”

Grace: Recognition of Proximity-Based Intentional Groups Using Collaborative Mobile Devices

“…For examples, [25] utilizes both WiFi and Bluetooth RSSI to calculate distance between two devices for relative positioning since the RSSI from a single radio has large error (e.g., 3.4 meters' average error using Bluetooth and 3.91 meters' average error using WiFi); [26] utilizes Bluetooth and combines Pedestrian Dead Reckoning (PDR) to estimate position relation between two pedestrians; [27] also utilizes Bluetooth and PDR to provide relative positions of surrounding people in the crowd and identify groups of people who move together. However, since PDR is not applicable to identify a group of people who are not moving, and using WiFi to assist proximity estimation consumes much more energy, a better solution is to use only Bluetooth but improving its accuracy.…”

Souk : Spatial Observation of Human Kinetics

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