Robust adaptive beamforming based on a new steering vector estimation algorithm

Projection-based robust adaptive beamforming with quadratic constraint

“…6 displays performance of different methods and, since look direction error and array perturbations are randomly generated, desired steering vector may exceed the bounds of corresponding constraints for [5,6] with output SINR degrading severely. The method in [7] can accommodate mutual coupling sufficiently, but the ability to resist sensor gain, and phase and location errors is limited. Since the construction of INCM in [13] is based on ideal array geometry, the occurrence of array geometry perturbations results in severe performance degradation.…”

“…7. Performance of [7] degrades when SNR¼ À10 dB, resulting from instability of the solution to optimization problem therein. The reason for performance degradation of [13] is consistent with Simulation Example 3.…”

“…Through maximizing beamformer output power, an advanced beamformer [6] is proposed to estimate desired signal steering vector based on angular sector where desired signal is located. A method utilizing an orthogonal interference-plus-noise subspace projection matrix is introduced in [7] and the dimension of estimated interference-plus-noise subspace is determined by an energy percentage parameter while no clear selection guideline is provided. Jiang et al proposed a class of beamformers exploiting non-Gaussianity of the signals [8][9][10], where minimum dispersion criterion is adopted in place of conventional minimum variance criterion.…”

A robust adaptive beamforming method based on iterative optimization algorithm

“…However, if there are errors between the presumed steering vector and the true one, however, it can greatly degrade the SINR performance as the desired signal is also treated as a target to be rejected. [1] Therefore, many approaches have been proposed to overcome the mismatch problem and improve the robustness of the adaptive beamformer. [2][3][4][5][6] In [2], Zhuang proposes a robust beamforming method which assumes the true steering vector belonging to a known subspace which can be used to design a robust array beamformer mitigating the problem of poor signal-subspace estimation.…”

“…[7] Recently, some robust beamforming approaches using as little as possible prior information have been proposed. [1,5] One common ground of these approaches is that they all use an integration matrix related to the array steering vector within some special angular sectors. This implies that in these approaches, the array antenna is assumed without any calibration error.…”