Autonomous Multi-Floor Localization Based on Smartphone-Integrated Sensors and Pedestrian Indoor Network

Abstract

Autonomous localization without local wireless facilities is proven as an efficient way for realizing location-based services in complex urban environments. The precision of the current map-matching algorithms is subject to the poor ability of integrated sensor-based trajectory estimation and the efficient combination of pedestrian motion information and the pedestrian indoor network. This paper proposes an autonomous multi-floor localization framework based on smartphone-integrated sensors and pedestrian network matching (ML-ISNM). A robust data and model dual-driven pedestrian trajectory estimator is proposed for accurate integrated sensor-based positioning under different handheld modes and disturbed environments. A bi-directional long short-term memory (Bi-LSTM) network is further applied for floor identification using extracted environmental features and pedestrian motion features, and further combined with the indoor network matching algorithm for acquiring accurate location and floor observations. In the multi-source fusion procedure, an error ellipse-enhanced unscented Kalman filter is developed for the intelligent combination of a trajectory estimator, human motion constraints, and the extracted pedestrian network. Comprehensive experiments indicate that the presented ML-ISNM achieves autonomous and accurate multi-floor positioning performance in complex and large-scale urban buildings. The final evaluated average localization error was lower than 1.13 m without the assistance of wireless facilities or a navigation database.

Distributed Consensus Algorithms in Sensor Networks with Higher-Order Topology

“…Sensor networks are widely deployed in environment for data gathering and monitoring purposes [ 1 , 2 ]. Originally motivated in military surveillance and then popularized to mobile and wireless communication, a large number of low-cost radars, endowed with communication capability, are distributedly located to fulfill tasks such as cooperative positioning and target recognition [ 3 , 4 , 5 , 6 , 7 ]. In typical applications, sensors collaborate to reach a consensus that accurately represents the correct classification of an event or the underlying true state of nature through a predefined communication network that is usually described by a connected graph and suffers from communication constraints [ 8 ].…”