Direction of arrival estimation using canonical and crystallographic volumetric element configurations

Xia, Zheng; Huff, Gregory H.; Chamberland, Jean-François; Pfister, Henry D.; Bhattacharya, Raktim

doi:10.1109/eucap.2012.6206123

Cited by 5 publications

(3 citation statements)

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“…However, it requires additional hardware and computational cost. Furthermore, the uniform linear and planar array will cause the ambiguity problem (angle aliasing) due to the symmetry of array structures [20,21,22]. Xia et al proposed that cubic arrays still have ambiguity problem and the spherical array can significantly reduce this problem [22].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the uniform linear and planar array will cause the ambiguity problem (angle aliasing) due to the symmetry of array structures [20,21,22]. Xia et al proposed that cubic arrays still have ambiguity problem and the spherical array can significantly reduce this problem [22]. Recently, 3D antenna array configurations have attracted much more research interest in array signal processing [22,23,24,25,26,27,28].…”

Section: Introductionmentioning

confidence: 99%

“…Xia et al proposed that cubic arrays still have ambiguity problem and the spherical array can significantly reduce this problem [22]. Recently, 3D antenna array configurations have attracted much more research interest in array signal processing [22,23,24,25,26,27,28]. Most of those 3D arrays above are constructed from regular structure (i.e., cubic, cylinder), extending the planar array (i.e., URA, UCA) or configuring the virtual 3D array based on the planar array.…”

Section: Introductionmentioning

confidence: 99%

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

Chen

Yeh

Chamberland

et al. 2019

Sensors

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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.

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