MISSION-ORIENTED SENSOR ARRAYS AND UAVs – A CASE STUDY ON ENVIRONMENTAL MONITORING

This paper presents a new concept of UAV mission design in geomatics, applied to the generation of thematic maps for a multitude of civilian and military applications. We discuss the architecture of Mission-Oriented Sensors Arrays (MOSA), proposed in Figueira et Al. (2013), aimed at splitting and decoupling the mission-oriented part of the system (non safety-critical hardware and software) from the aircraft control systems (safety-critical). As a case study, we present an environmental monitoring application for the automatic generation of thematic maps to track gunshot activity in conservation areas. The MOSA modeled for this application integrates information from a thermal camera and an on-the-ground microphone array. The use of microphone arrays technology is of particular interest in this paper. These arrays allow estimation of the direction-of-arrival (DOA) of the incoming sound waves. Information about events of interest is obtained by the fusion of the data provided by the microphone array, captured by the UAV, fused with information from the termal image processing. Preliminary results show the feasibility of the on-the-ground sound processing array and the simulation of the main processing module, to be embedded into an UAV in a future work. The main contributions of this paper are the proposed MOSA system, including concepts, models and architecture.

[1]  Reg Austin,et al.  Unmanned Aircraft Systems: Uavs Design, Development and Deployment , 2010 .

[2]  Viacheslav I. Adamchuk,et al.  On-the-go soil sensors for precision agriculture , 2004 .

[3]  Maria-Lluïsa Marsal-Llacuna,et al.  Lessons in urban monitoring taken from sustainable and livable cities to better address the Smart Cities initiative , 2015 .

[4]  Abdulaziz Al-Humam,et al.  Service-oriented architectures for safety-critical systems , 2015 .

[5]  Onofre Trindade,et al.  A layered approach to design autopilots , 2010, 2010 IEEE International Conference on Industrial Technology.

[6]  Kalinka R. L. J. C. Branco,et al.  Smart Sensor Protocol - a new standard for UAV and payload integration , 2014, 2014 International Conference on Unmanned Aircraft Systems (ICUAS).

[7]  G. Carter,et al.  The generalized correlation method for estimation of time delay , 1976 .

[8]  Pratap Tokekar,et al.  Sensor Planning for a Symbiotic UAV and UGV System for Precision Agriculture , 2016, IEEE Trans. Robotics.

[9]  Görres Grenzdörffer,et al.  THE PHOTOGRAMMETRIC POTENTIAL OF LOW-COST UAVs IN FORESTRY AND AGRICULTURE , 2008 .

[10]  J. Hummelb,et al.  On-the-go soil sensors for precision agriculture , 2004 .

[11]  Dario Floreano,et al.  Robust acoustic source localization of emergency signals from Micro Air Vehicles , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[12]  Alex Bateman,et al.  An introduction to hidden Markov models. , 2007, Current protocols in bioinformatics.

[13]  Michael S. Brandstein,et al.  Microphone Arrays - Signal Processing Techniques and Applications , 2001, Microphone Arrays.

[14]  Luis J. Villanueva-Rivera,et al.  Soundscape Ecology: The Science of Sound in the Landscape , 2011 .

[15]  Izabela Lyon Freire.  Robust direction-of-arrival by matched-lags, applied to gunshots , 2014 .

[16]  J. J. Caffery,et al.  A new approach to the geometry of TOA location , 2000, Vehicular Technology Conference Fall 2000. IEEE VTS Fall VTC2000. 52nd Vehicular Technology Conference (Cat. No.00CH37152).

[17]  K. Nurminen,et al.  ON GEOMETRIC PROCESSING OF MULTI-TEMPORAL IMAGE DATA COLLECTED BY LIGHT UAV SYSTEMS , 2012 .

[18]  P. Mermelstein,et al.  Distance measures for speech recognition, psychological and instrumental , 1976 .

[19]  David G. Schmale,et al.  Development and application of an autonomous unmanned aerial vehicle for precise aerobiological sampling above agricultural fields , 2008, J. Field Robotics.