4.4 Article

Cognitive FDA-MIMO radar for LPI transmit beamforming

期刊

IET RADAR SONAR AND NAVIGATION
卷 11, 期 10, 页码 1574-1580

出版社

INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-rsn.2016.0551

关键词

radar antennas; radar signal processing; MIMO radar; array signal processing; radar receivers; probability; transmitting antennas; antenna radiation patterns; radar detection; concave programming; signal classification; cognitive FDA-MIMO radar; illegal eavesdroppers; high-gain beamscanning signals; active radar visibility reduction; low probability of intercept capability; LPI capability; cognitive LPI transmit beamforming; frequency diverse array; FDA; multiple-input multiple-output hybrid array antenna; range-angle-dependent transmitting beampattern; beam power minimisation; radar receiver; radar detection performance; target range estimation; direction of arrival estimation; two-dimensional multiple signal classification algorithm; transmit beamforming optimisation; nonconvex optimisation; linear combination; nonlinear combination; closed form solution

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Active radar is vulnerable to illegal eavesdroppers due to its high-gain beam-scanning signals. To reduce active radar visibility and enhance its low probability of intercept (LPI) capability, this study proposes a cognitive LPI transmit beamforming scheme using frequency diverse array (FDA) and multiple-input multiple-output (MIMO) hybrid array antenna. The achievement of LPI is due to the unique range-angle-dependent transmitting beampattern of FDA-MIMO radar, which minimises the beam power at the target location to reduce its visibility and simultaneously maximise the power at the radar receiver without degrading the radar detection performance. Furthermore, the FDA-MIMO radar operates in a cognitive way: the receiver estimates the target range and the direction of arrival with a two-dimensional multiple signal classification algorithm, and feedbacks their estimates to the transmitter to update the FDA-MIMO transmit beamforming. As the transmit beamforming optimisation is non-convex problem, the authors propose three methods, namely linear combination, non-linear combination and closed form solution. All the proposed methods are verified by simulation results.

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