Open AccessCovariance matrix reconstruction and steering vector estimation for robust adaptive transmit/receive beamforming in full phased‐MIMO radar
Author(s) -
Lu J.,
Yang J.,
Hou B.,
Zhu F.,
Liu G.
Publication year - 2021
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/ell2.12120
Subject(s) - beamforming , phased array , mimo , computer science , covariance matrix , radar , electronic engineering , adaptive beamformer , algorithm , continuous wave radar , control theory (sociology) , engineering , telecommunications , radar imaging , antenna (radio) , artificial intelligence , control (management)
The hybrid phased multiple‐input‐multiple‐output (MIMO) radar with subarray partition can provide the transmit coherent gain of phased array and the waveform diversity of MIMO radar. The current researches mainly focus on the different subarray partition schemes of the transmit array. In this letter, a full phased‐MIMO (FPMIMO) radar with equally overlapped subarrays is introduced, and a robust adaptive transmit/receive beamforming algorithm is proposed based on the virtual interference‐plus‐noise covariance matrix (INCM) reconstruction and the virtual steering vector estimation. In the FPMIMO radar, the conventional beamforming is used for the signal transmission and reception on each subarray, and all subarrays can be assumed as a uniform linear array. Based on the presumed uniform linear array, the virtual INCM can be reconstructed by Eigen‐decomposition of the reconstructed INCM, and the virtual steering vector can be obtained by the estimated steering vector. With fewer antenna elements and sampling channels in the FPMIMO radar, simulation results demonstrate better performance of the proposed algorithm in a wide range of input signal‐to‐noise ratio.