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

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Abstract

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.

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Authors and Affiliations

  1. Utah Water Research Laboratory, Utah State University, Logan, UT, 84321, USA

    Austin M. Jensen

  2. Mechanical and Aerospace Engineering Department, Utah State University, Logan, UT, 84321, USA

    David K. Geller

  3. Mechatronics, Embedded Systems and Automation (MESA) Lab, University of California, Merced, Merced, CA, 95343, USA

    YangQuan Chen

Authors
  1. Austin M. Jensen
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  2. David K. Geller
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  3. YangQuan Chen
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Correspondence to Austin M. Jensen.

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Jensen, A.M., Geller, D.K. & Chen, Y. Monte Carlo Simulation Analysis of Tagged Fish Radio Tracking Performance by Swarming Unmanned Aerial Vehicles in Fractional Order Potential Fields. J Intell Robot Syst 74, 287–307 (2014). https://doi.org/10.1007/s10846-013-9949-9

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  • DOI: https://doi.org/10.1007/s10846-013-9949-9

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