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Open Access

Efficient Signal Separation Method Based on Antenna Arrays for GNSS Meaconing

Department of Electronic Engineering, Tsinghua University, Beijing 100084, China.
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Abstract

As an effective deceptive interference technique for military navigation signals, meaconing can be divided into two main types: those that replay directly and those that replay after signal separation. The latter can add different delays to each satellite signal and mislead the victim receiver with respect to any designated position, thus has better controllability and concealment capability. A previous study showed there to be two main spatial processing techniques for separating military signals, whereby either multiple large-caliber antennas or antenna arrays are used to form multiple beams that align with all visible satellites. To ensure sufficient spatial resolution, the main lobe width of the antenna or beam must be sufficiently narrow, which requires the use of a large antenna aperture or a large number of array elements. In this paper, we propose a convenient and effective signal separation method, which is based on an antenna array with fewer elements. While the beam of the array is pointing to a specified satellite, the other satellite signals are regarded as interference and their power is suppressed to a level below the receiver’s sensitivity. With this method, the number of array elements depends only on the number of visible satellites, thus greatly reducing the hardware cost and required processing capacity.

Keywords

GNSS meaconingantenna arraybeamformingsignal separation

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Tsinghua Science and Technology
Volume 24 Issue 2,
April 2019
Pages 216-225
DOI: 10.26599/TST.2018.9010125
Cite this article:
Zhang J, Cui X, Xu H, et al. Efficient Signal Separation Method Based on Antenna Arrays for GNSS Meaconing. Tsinghua Science and Technology, 2019, 24(2): 216-225. https://doi.org/10.26599/TST.2018.9010125

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Received: 11 November 2017
Revised: 22 November 2017
Accepted: 27 November 2017
Published: 31 December 2018
© The author(s) 2019
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