Abstract
The performance of adaptive algorithms, including direct data domain least square, can be significantly degraded in the presence of mutual coupling among array elements. In this paper, a new adaptive algorithm was proposed for the fast recovery of the signal with one snapshot of receiving signals in the presence of mutual coupling, based on the two-dimensional direct data domain least squares (2-D D3LS) for uniform rectangular array (URA). In this method, inverse mutual coupling matrix was not computed. Thus, the computation was reduced and the signal recovery was very fast. Taking mutual coupling into account, a method was derived for estimation of the coupling coefficient which can accurately estimate the coupling coefficient without any auxiliary sensors. Numerical simulations show that recovery of the desired signal is accurate in the presence of mutual coupling.
Highlights
Adaptive antenna arrays are strongly affected by the existence of mutual coupling (MC) effect between antenna elements; if the effects of MC are ignored, the system performance will not be accurate [1, 2]
Many efforts have been made to compensate for the MC effect for uniform linear array (ULA) and uniform circular array (UCA) [2,3,4,5,6,7,8,9]
In [9], a new method was proposed to compensate for the MC effect which relied on the calculation of a new definition of mutual impedance. the authors did not deal with 2-D DOA estimation problem
Summary
Adaptive antenna arrays are strongly affected by the existence of mutual coupling (MC) effect between antenna elements; if the effects of MC are ignored, the system performance will not be accurate [1, 2]. Many algorithms of the 1-D DOA estimation have been extended to solve the 2-D cases [10, 11]; a few have considered the effect of mutual coupling or any other array errors [12] Most of these proposed adaptive algorithms are based on the covariance matrix of the interference. The following equation is derived to recover the desired signal in the presence of mutual coupling (Proof in the appendix), notwithstanding to compute the inverse matrix of MC. This equation could be reduced the computation of the algorithm
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