In the conventional robust adaptive beamformers (RABs), a common problem is that the estimated manifold vector of the signal of interest (SOI) may converge to the interference subspace. By using the data dependent constraints, such problem can be mitigated. In this paper, we propose to employ the oblique projection to obtain the SOI covariance matrix and compute the interference covariance matrix in an estimate-and-subtract manner. Rather than using the semidefinite programming relaxation, we find that the proposed RAB, which belongs to the well known non-convex quadratically constrained quadratic programming problem, can be solved efficiently by the Lagrange multiplier methodology coupled with the Newton’s method. Our simulation results demonstrate the superiority of the proposed method over other previously developed RAB techniques.

在传统的鲁棒自适应波束形成器 (RAB) 中，一个常见问题是感兴趣信号 (SOI) 的估计流形矢量可能会收敛到干扰子空间。通过使用依赖于数据的约束，可以减轻这样的问题。在本文中，我们建议采用斜投影来获得 SOI 协方差矩阵，并以估计和减去的方式计算干扰协方差矩阵。我们发现所提出的 RAB 不是使用半定规划松弛，而是属于众所周知的非凸二次约束二次规划问题，可以通过拉格朗日乘子方法与牛顿方法有效地解决。我们的模拟结果证明了所提出的方法优于其他先前开发的 RAB 技术。