ICUAS'17 Paper Abstract


Paper ThB5.5

Hasanzade, Mehmet (Aerospace Research Center, Istanbul Technical University), Herekoglu, Omer (Aerospace Research Center, Istanbul Technical University), ÜRE, Nazim Kemal (Massachusetts Institute of Technology), Koyuncu, Emre (Aerospace Research Center, Istanbul Technical University), Yeniçeri, Ramazan (Aerospace Research Center, Istanbul Technical University), INALHAN, GOKHAN (ISTANBUL TECHNICAL UNIVERSITY)

Localization and Tracking of RF Emitting Targets with Multiple Unmanned Aerial Vehicles in Large Scale Environments with Uncertain Transmitter Power

Scheduled for presentation during the "Sensor Fusion - II" (ThB5), Thursday, June 15, 2017, 15:05−15:25, San Marco Island

2017 International Conference on Unmanned Aircraft Systems, June 13-16, 2017, Miami Marriott Biscayne Bay, Miami, FL,

This information is tentative and subject to change. Compiled on April 12, 2021

Keywords UAS Applications, Simulation, Sensor Fusion


In this paper we study the localization and tracking of a radio frequency (RF) emitting target using multiple unmanned aerial vehicles (UAVs) over a large scale environment. Although localization of RF emitting targets using multiple measurements is a well studied problem, the standard approaches become inefficient when the signal power is uncertain and there is significant noise in the received signal strength (RSS) when the search environment is large scale. We present a localization and tracking architecture, where a data driven neural network model is used for estimating the unknown signal strength and extended Kalman filters are utilized for eliminating the RSS noise and increase the precision of target tracking performance. We present simulation results in a $10 times 10 text{ km}^2$ search area, where $3$ fixed wing UAVs localize and track a target with up to $28.3 text{ m}$ average error distance.



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