Journal of Intelligent & Robotic Systems

, Volume 74, Issue 1–2, pp 287–307 | Cite as

Monte Carlo Simulation Analysis of Tagged Fish Radio Tracking Performance by Swarming Unmanned Aerial Vehicles in Fractional Order Potential Fields

  • Austin M. Jensen
  • David K. Geller
  • YangQuan Chen


Tracking fish using implanted radio transmitters is an important part of studying and preserving native fish species. However, conventional methods for locating the fish after they are tagged can be time consuming and costly. Unmanned Aerial Vehicles (UAV) have been used in general radio localization applications and can possibly be used to locate fish quickly and effectively. However, the methods developed for multi-UAV navigation and transmitter localization are complex and might not work well for practical and routine use. This work focuses on developing simple methods for multi-UAV navigation and transmitter localization. A real-world simulator is created to test these methods; it includes a signal propagation model based on actual data from a UAV. Swarm-like navigation methods (using potential fields) are used for multi-UAV navigation, and an Extended Kalman Filter is used, along with a simplified version of the propagation model, to estimate the location of the transmitter. Multiple navigation methods are introduced and compared using Monte Carlo Analysis. Despite a noisy signal and a simplified measurement model, the different navigation methods are able to estimate the location of the transmitter with one or more UAVs.


Unmanned aerial vehicle UAV Swarm AggieAir Potential field navigation Kalman filter Radio localization Fish tracking Monte Carlo analysis UAV simulation Wireless propagation model 


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  1. 1.
    Chen, Y., Wang, Z., Moore, K.: Optimal spraying control of a diffusion process using mobile actuator networks with fractional potential field based dynamic obstacle avoidance. In: Proc. of the IEEE International Conference on Networking, Sensing and Control, pp. 107–112. IEEE (2006)Google Scholar
  2. 2.
    Frew, E.W., Dixon, C., Argrow, B., Brown, T.: Radio source localization by a cooperating UAV team. In: Infotech@ Aerospace (2005)Google Scholar
  3. 3.
    Ge, S., Cui, Y.: Dynamic motion planning for mobile robots using potential field method. Auton. Robot. 13(3), 207–222 (2002)CrossRefzbMATHGoogle Scholar
  4. 4.
    Gray, R.H., Haynes, J.M.: Spawning migration of adult chinook salmon (oncorhynchus tshawytscha) carrying external and internal radio transmitters. J. Fish. Res. Board Can. 36(9), 1060–1064 (1979)CrossRefGoogle Scholar
  5. 5.
    Hillyard, R.W., Keeley, E.R.: Distribution and spawning migrations of fluvial bonneville cutthroat trout in the Bear River, Idaho. Tech. Rep., Idaho State University (2009)Google Scholar
  6. 6.
    Jensen, A., Chen, Y.: Tracking tagged fish with swarming unmanned aerial vehicles using fractional order potential fields and Kalman filtering. In: Proc. of the International Conference on Unmanned Aircraft Systems (ICUAS) (2013)Google Scholar
  7. 7.
    Jensen, A.M., Hardy, T., McKee, M., Chen, Y.Q.: Using a multispectral autonomous unmanned aerial remote sensing platform (AggieAir) for Riparian and Wetland applications. In: Proc. IEEE Int. Geoscience and Remote Sensing Symp. (IGARSS) (2011)Google Scholar
  8. 8.
    Jensen, A.M., McKee, M., Chen, Y.: Calibrating thermal imagery from an unmanned aerial system—AggieAir. In: Proc. IEEE Int. Geoscience and Remote Sensing Symp. (IGARSS) (2013)Google Scholar
  9. 9.
    Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots. In: Proc. of the IEEE International Conference on Robotics and Automation, vol. 2, pp. 500–505. Institute of Electrical and Electronics Engineers (1985)Google Scholar
  10. 10.
    Korner, F., Speck, R., Goktogan, A.H., Sukkarieh, S.: Autonomous airborne wildlife tracking using radio signal strength. In: Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 107–112. IEEE (2010)Google Scholar
  11. 11.
    Leonardo, M., Jensen, A., Coopmans, C., McKee, M., Chen, Y.: A miniature wildlife tracking UAV payload system using acoustic biotelemetry. In: Proc. of the ASME International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (2013)Google Scholar
  12. 12.
    Miller, K.: The Weyl fractional calculus. In: Ross, B. (ed.) Fractional Calculus and its Applications. Lecture Notes in Mathematics, vol. 457, pp. 80–89. Springer Berlin Heidelberg (1975)CrossRefGoogle Scholar
  13. 13.
    Paul, T., Krogstad, T.R., Gravdahl, J.T.: Modelling of UAV formation flight using 3D potential field. Simul. Model. Pract. Theory 16(9), 1453–1462 (2008)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Austin M. Jensen
    • 1
  • David K. Geller
    • 2
  • YangQuan Chen
    • 3
  1. 1.Utah Water Research LaboratoryUtah State UniversityLoganUSA
  2. 2.Mechanical and Aerospace Engineering DepartmentUtah State UniversityLoganUSA
  3. 3.Mechatronics, Embedded Systems and Automation (MESA) LabUniversity of California, MercedMercedUSA

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