3 research outputs found
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
Study of Robust Distributed Diffusion RLS Algorithms with Side Information for Adaptive Networks
This work develops robust diffusion recursive least squares algorithms to
mitigate the performance degradation often experienced in networks of agents in
the presence of impulsive noise. The first algorithm minimizes an exponentially
weighted least-squares cost function subject to a time-dependent constraint on
the squared norm of the intermediate update at each node. A recursive strategy
for computing the constraint is proposed using side information from the
neighboring nodes to further improve the robustness. We also analyze the
mean-square convergence behavior of the proposed algorithm. The second proposed
algorithm is a modification of the first one based on the dichotomous
coordinate descent iterations. It has a performance similar to that of the
former, however its complexity is significantly lower especially when input
regressors of agents have a shift structure and it is well suited to practical
implementation. Simulations show the superiority of the proposed algorithms
over previously reported techniques in various impulsive noise scenarios.Comment: 15 figures, 17 page
Study of Diffusion Normalized Least Mean M-estimate Algorithms
This work proposes diffusion normalized least mean M-estimate algorithm based
on the modified Huber function, which can equip distributed networks with
robust learning capability in the presence of impulsive interference. In order
to exploit the system's underlying sparsity to further improve the learning
performance, a sparse-aware variant is also developed by incorporating the
-norm of the estimates into the update process. We then analyze the
transient, steady-state and stability behaviors of the algorithms in a unified
framework. In particular, we present an analytical method that is simpler than
conventional approaches to deal with the score function since it removes the
requirements of integrals and Price's theorem. Simulations in various impulsive
noise scenarios show that the proposed algorithms are superior to some existing
diffusion algorithms and the theoretical results are verifiable.Comment: 14 pages, 13 figure