Title: A big data-based RF localisation method for unmanned search and rescue
Authors: Ju Wang; Hongzhe Liu; Hong Bao; Cesar Flores Montoya; James Hinton
Addresses: Virginia State University, Hayden Rd, Petersburg, VA, USA ' Beijing Union University, 97 Beishihuan Rd, Beijing, China ' Beijing Union University, 97 Beishihuan Rd, Beijing, China ' Virginia State University, Hayden Rd, Petersburg, VA, USA ' Virginia State University, Hayden Rd, Petersburg, VA, USA
Abstract: Autonomous mobile robots require efficient big-data methods to process a large amount of real-time sensory data to perform a task. We investigate a novel RF sensing-based method for target localisation where a large set of sensor data are mined to produce meaningful location information of a target device. The estimated location of the target is further used by the navigation algorithm to execute a movement plan. Using the networked RF beacon data, the proposed big data approach alleviates the problem of noisy RF measurements in location estimation. A particle filter algorithm is used to track the location of target node. The algorithm demonstrates a beyond-the-grid accuracy even only a coarse RF map is used.
Keywords: RF mapping; robot localisation; navigation; measurement mining.
International Journal of Big Data Intelligence, 2018 Vol.5 No.1/2, pp.124 - 132
Received: 13 Mar 2016
Accepted: 29 Jul 2016
Published online: 01 Dec 2017 *