2 research outputs found
Direction Finding Algorithms with Joint Iterative Subspace Optimization
In this paper, a reduced-rank scheme with joint iterative optimization is
presented for direction of arrival estimation. A rank-reduction matrix and an
auxiliary reduced-rank parameter vector are jointly optimized to calculate the
output power with respect to each scanning angle. Subspace algorithms to
estimate the rank-reduction matrix and the auxiliary vector are proposed.
Simulations are performed to show that the proposed algorithms achieve an
enhanced performance over existing algorithms in the studied scenarios.Comment: 11 figures, 4 tables. IEEE Transactions on Aerospace and Electronic
Systems, 201
Design of Robust Adaptive Beamforming Algorithms Based on Low-Rank and Cross-Correlation Techniques
This work presents cost-effective low-rank techniques for designing robust
adaptive beamforming (RAB) algorithms. The proposed algorithms are based on the
exploitation of the cross-correlation between the array observation data and
the output of the beamformer. Firstly, we construct a general linear equation
considered in large dimensions whose solution yields the steering vector
mismatch. Then, we employ the idea of the full orthogonalization method (FOM),
an orthogonal Krylov subspace based method, to iteratively estimate the
steering vector mismatch in a reduced-dimensional subspace, resulting in the
proposed orthogonal Krylov subspace projection mismatch estimation (OKSPME)
method. We also devise adaptive algorithms based on stochastic gradient (SG)
and conjugate gradient (CG) techniques to update the beamforming weights with
low complexity and avoid any costly matrix inversion. The main advantages of
the proposed low-rank and mismatch estimation techniques are their
cost-effectiveness when dealing with high dimension subspaces or large sensor
arrays. Simulations results show excellent performance in terms of the output
signal-to-interference-plus-noise ratio (SINR) of the beamformer among all the
compared RAB methods.Comment: 11 figures, 12 page