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A Sensor-Driven Analysis of Distributed Direction Finding Systems Based on UAV Swarms

1
Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843-3128, USA
2
School of Electrical Engineering and Computer Science, Pennsylvania State University, University Park, PA 16802, USA
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(12), 2659; https://doi.org/10.3390/s19122659
Received: 29 April 2019 / Revised: 6 June 2019 / Accepted: 8 June 2019 / Published: 12 June 2019
(This article belongs to the Special Issue Optimization and Communication in UAV Networks)
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Abstract

This paper reports on the research of factors that impact the accuracy and efficiency of an unmanned aerial vehicle (UAV) based radio frequency (RF) and microwave data collection system. The swarming UAVs (agents) can be utilized to create micro-UAV swarm-based (MUSB) aperiodic antenna arrays that reduce angle ambiguity and improve convergence in sub-space direction-of-arrival (DOA) techniques. A mathematical data model is addressed in this paper to demonstrate fundamental properties of MUSB antenna arrays and study the performance of the data collection system framework. The Cramer–Rao bound (CRB) associated with two-dimensional (2D) DOAs of sources in the presence of sensor gain and phase coefficient is derived. The single-source case is studied in detail. The vector-space of emitters is exploited and the iterative-MUSIC (multiple signal classification) algorithm is created to estimate 2D DOAs of emitters. Numerical examples and practical measurements are provided to demonstrate the feasibility of the proposed MUSB data collection system framework using iterative-MUSIC algorithm and benchmark theoretical expectations. View Full-Text
Keywords: direction-of-arrival estimation; unmanned aerial vehicles; UAV swarm; aperiodic arrays; MUSIC; Cramer–Rao bound direction-of-arrival estimation; unmanned aerial vehicles; UAV swarm; aperiodic arrays; MUSIC; Cramer–Rao bound
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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MDPI and ACS Style

Chen, Z.; Yeh, S.; Chamberland, J.-F.; Huff, G.H. A Sensor-Driven Analysis of Distributed Direction Finding Systems Based on UAV Swarms. Sensors 2019, 19, 2659.

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