Using perceived direction information for anchorless relative indoor localization. (1st September 2020)

• Record Type: Journal Article
• Authors: Hernandez, Steven M.; Bulut, Eyuphan
• Is Part Of: Journal of network and computer applications. Volume 165(2020)
• Publication Date: 2020-09-01
• Subjects: Indoor localization -- Monte Carlo algorithm; Microcomputers -- Periodicals; Computer networks -- Periodicals; Application software -- Periodicals; Micro-ordinateurs -- Périodiques; Réseaux d'ordinateurs -- Périodiques; Logiciels d'application -- Périodiques; Application software; Computer networks; Microcomputers; Periodicals; 004.05; 004
• DOI: 10.1016/j.jnca.2020.102714
• Languages: English
• ISSNs: 1084-8045

Abstract

Abstract: Identifying the positions of mobile devices within indoor environments allows for the development of advanced context-based applications and general environmental awareness. Classic localization methods require GPS; an expensive, high power consuming and inaccurate solution for indoor scenarios. Relative positioning instead allows nodes to recognize location in relation to neighboring nodes without the requirement of GPS. To triangulate their own position however, indoor localization methods either use Received Signal Strength Indication (RSSI) retrieved from neighboring devices to determine distance or simple binary contact information denoting whether two nodes are in communication range of one another. RSSI however is plagued by many sources of noise, thus decreasing distance prediction accuracy as well as being unreliable for networks of heterogeneous devices. Further, using only binary contacts provides a limited information for localization. In our work, we first demonstrate the unreliable nature of RSSI in heterogeneous networks. We then demonstrate our intermediate solution between unreliable RSSI and oversimplified binary classifications by introducing Perceived Direction Information (PDI) composed of three states: approaching, retreating and invisible. Through real world experiments, we demonstrate that PDI can be predicted using a Dense Neural Network with more than 95 % accuracy even on devices not used during training. We then describe an anchorless Monte Carlo Localization (MCL) algorithm which uses PDI to achieve higher accuracy and a reduction of communication over the state-of-the-art MCL based methods.