Feasibility of Discriminating UAV Propellers Noise from Distress Signals to Locate People in Enclosed Environments Using MEMS Microphone Arrays

Abstract

first_pagesettingsOrder Article Reprints Open AccessArticle Feasibility of Discriminating UAV Propellers Noise from Distress Signals to Locate People in Enclosed Environments Using MEMS Microphone Arrays by Alberto Izquierdo 1,*ORCID,Lara del Val 2ORCID,Juan J. Villacorta 1ORCID,Weikun Zhen 3,Sebastian Scherer 3 andZheng Fang 4ORCID 1 Signal Theory and Communications Department, University of Valladolid, 47011 Valladolid, Spain 2 Mechanical Engineering Area, Industrial Engineering School, University of Valladolid, 47011 Valladolid, Spain 3 Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15289, USA 4 Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110819, China * Author to whom correspondence should be addressed. Sensors 2020, 20(3), 597; https://doi.org/10.3390/s20030597 Submission received: 10 October 2019 / Revised: 9 January 2020 / Accepted: 17 January 2020 / Published: 21 January 2020 (This article belongs to the Special Issue Sensors for Unmanned Aircraft Systems and Related Technologies) Downloadkeyboard_arrow_down Browse Figures Versions Notes Abstract Detecting and finding people are complex tasks when visibility is reduced. This happens, for example, if a fire occurs. In these situations, heat sources and large amounts of smoke are generated. Under these circumstances, locating survivors using thermal or conventional cameras is not possible and it is necessary to use alternative techniques. The challenge of this work was to analyze if it is feasible the integration of an acoustic camera, developed at the University of Valladolid, on an unmanned aerial vehicle (UAV) to locate, by sound, people who are calling for help, in enclosed environments with reduced visibility. The acoustic array, based on MEMS (micro-electro-mechanical system) microphones, locates acoustic sources in space, and the UAV navigates autonomously by closed enclosures. This paper presents the first experimental results locating the angles of arrival of multiple sound sources, including the cries for help of a person, in an enclosed environment. The results are promising, as the system proves able to discriminate the noise generated by the propellers of the UAV, at the same time it identifies the angles of arrival of the direct sound signal and its first echoes reflected on the reflective surfaces.