Combining LoRaWAN and a New 3D Motion Model for Remote UAV Tracking

Over the last few years, the many uses of Unmanned Aerial Vehicles (UAVs) have captured the interest of both the scientific and the industrial communities. A typical scenario consists in the use of UAVs for surveillance or target-search missions over a wide geographical area. In this case, it is fundamental for the command center to accurately estimate and track the trajectories of the UAVs by exploiting their periodic state reports. In this work, we design an ad hoc tracking system that exploits the Long Range Wide Area Network (LoRaWAN) standard for communication and an extended version of the Constant Turn Rate and Acceleration (CTRA) motion model to predict drone movements in a 3D environment. Simulation results on a publicly available dataset show that our system can reliably estimate the position and trajectory of a UAV, significantly outperforming baseline tracking approaches.

[1]  Özgür B. Akan,et al.  Energy Neutral Internet of Drones , 2018, IEEE Communications Magazine.

[2]  Ranveer Chandra,et al.  FarmBeats: An IoT Platform for Data-Driven Agriculture , 2017, NSDI.

[3]  Stjepan Bogdan,et al.  Decentralized planning and control for UAV–UGV cooperative teams , 2018, Autonomous Robots.

[4]  Dharma P. Agrawal,et al.  Communication and networking of UAV-based systems: Classification and associated architectures , 2017, J. Netw. Comput. Appl..

[5]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[6]  Federico Chiariotti,et al.  Quality-Aware Broadcasting Strategies for Position Estimation in VANETs , 2019, EW.

[7]  A. Polychronopoulos,et al.  Unscented Kalman filter design for curvilinear motion models suitable for automotive safety applications , 2005, 2005 7th International Conference on Information Fusion.

[8]  Tao Ma,et al.  The study of UAV intelligent support mode based on battlefield networks , 2017, 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC).

[9]  Thomas Kunz,et al.  An enhanced Gauss-Markov mobility model for simulations of unmanned aerial ad hoc networks , 2014, 2014 7th IFIP Wireless and Mobile Networking Conference (WMNC).

[10]  Marco Pini,et al.  Loose and Tight GNSS/INS Integrations: Comparison of Performance Assessed in Real Urban Scenarios , 2017, Sensors.

[11]  Vincenzo Rosario Baraniello,et al.  UAV position and attitude estimation using IMU, GNSS and camera , 2012, 2012 15th International Conference on Information Fusion.

[12]  Ekram Hossain,et al.  Multi-Tier Drone Architecture for 5G/B5G Cellular Networks: Challenges, Trends, and Prospects , 2017, IEEE Communications Magazine.

[13]  Paul Y. Oh,et al.  Unmanned aerial and ground vehicle (UAV-UGV) system prototype for civil infrastructure missions , 2018, 2018 IEEE International Conference on Consumer Electronics (ICCE).

[14]  Kyongsu Yi,et al.  Probabilistic and Holistic Prediction of Vehicle States Using Sensor Fusion for Application to Integrated Vehicle Safety Systems , 2014, IEEE Transactions on Intelligent Transportation Systems.

[15]  Mahbub Hassan,et al.  Survey on UAV Cellular Communications: Practical Aspects, Standardization Advancements, Regulation, and Security Challenges , 2018, IEEE Communications Surveys & Tutorials.

[16]  Yi Zhou,et al.  A Smooth-Turn Mobility Model for Airborne Networks , 2013, IEEE Trans. Veh. Technol..

[17]  P. Moral.  Nonlinear filtering : Interacting particle resolution , 1997 .

[18]  Andrea Zanella,et al.  A Thorough Study of LoRaWAN Performance Under Different Parameter Settings , 2019, IEEE Internet of Things Journal.

[19]  Ingrid Moerman,et al.  LoRa Scalability: A Simulation Model Based on Interference Measurements , 2017, Sensors.

[20]  Marc Van Droogenbroeck,et al.  Mid-Air: A Multi-Modal Dataset for Extremely Low Altitude Drone Flights , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[21]  Christian Wietfeld,et al.  Design of an UWB indoor-positioning system for UAV navigation in GNSS-denied environments , 2015, 2015 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[22]  Ouns Bouachir,et al.  A mobility model for UAV ad hoc network , 2014, 2014 International Conference on Unmanned Aircraft Systems (ICUAS).

[23]  Miguel A. Olivares-Méndez,et al.  A real-time model predictive position control with collision avoidance for commercial low-cost quadrotors , 2016, 2016 IEEE Conference on Control Applications (CCA).

[24]  Andrey V. Savkin,et al.  Towards the Internet of Flying Robots: A Survey , 2018, Sensors.

[25]  Davide Scaramuzza,et al.  The Zurich urban micro aerial vehicle dataset , 2017, Int. J. Robotics Res..

[26]  Ahmad Fikri Abdullah,et al.  AERIAL PLATFORM RELIABILITY FOR FLOOD MONITORING UNDER VARIOUS WEATHER CONDITIONS: A REVIEW , 2018 .

[27]  Rudolph van der Merwe,et al.  Sigma-point kalman filters for probabilistic inference in dynamic state-space models , 2004 .

[28]  Bernhard Rinner,et al.  An Autonomous Multi-UAV System for Search and Rescue , 2015, DroNet@MobiSys.

[29]  Antoine B. Bagula,et al.  Internet-Of-Things in Motion: A UAV Coalition Model for Remote Sensing in Smart Cities , 2018, Sensors.