4 research outputs found
Maximum correntropy criterion based sparse adaptive filtering algorithms for robust channel estimation under non-Gaussian environments
Sparse adaptive channel estimation problem is one of the most important
topics in broadband wireless communications systems due to its simplicity and
robustness. So far many sparsity-aware channel estimation algorithms have been
developed based on the well-known minimum mean square error (MMSE) criterion,
such as the zero-attracting least mean square (ZALMS), which are robust under
Gaussian assumption. In non-Gaussian environments, however, these methods are
often no longer robust especially when systems are disturbed by random
impulsive noises. To address this problem, we propose in this work a robust
sparse adaptive filtering algorithm using correntropy induced metric (CIM)
penalized maximum correntropy criterion (MCC) rather than conventional MMSE
criterion for robust channel estimation. Specifically, MCC is utilized to
mitigate the impulsive noise while CIM is adopted to exploit the channel
sparsity efficiently. Both theoretical analysis and computer simulations are
provided to corroborate the proposed methods.Comment: 29 pages, 12 figures, accepted by Journal of the Franklin Institut
A Band-independent Variable Step Size Proportionate Normalized Subband Adaptive Filter Algorithm
Proportionate-type normalized suband adaptive filter (PNSAF-type) algorithms
are very attractive choices for echo cancellation. To further obtain both fast
convergence rate and low steady-state error, in this paper, a variable step
size (VSS) version of the presented improved PNSAF (IPNSAF) algorithm is
proposed by minimizing the square of the noise-free a posterior subband error
signals. A noniterative shrinkage method is used to recover the noise-free a
priori subband error signals from the noisy subband error signals.
Significantly, the proposed VSS strategy can be applied to any other PNSAF-type
algorithm, since it is independent of the proportionate principles. Simulation
results in the context of acoustic echo cancellation have demonstrated the
effectiveness of the proposed method.Comment: 21 pages,8 figures, 2 tables, accepted by AEU-International Journal
of Electronics and Communications on May 31, 201
Study of Sparsity-Aware Subband Adaptive Filtering Algorithms with Adjustable Penalties
We propose two sparsity-aware normalized subband adaptive filter (NSAF)
algorithms by using the gradient descent method to minimize a combination of
the original NSAF cost function and the l1-norm penalty function on the filter
coefficients. This l1-norm penalty exploits the sparsity of a system in the
coefficients update formulation, thus improving the performance when
identifying sparse systems. Compared with prior work, the proposed algorithms
have lower computational complexity with comparable performance. We study and
devise statistical models for these sparsity-aware NSAF algorithms in the mean
square sense involving their transient and steady -state behaviors. This study
relies on the vectorization argument and the paraunitary assumption imposed on
the analysis filter banks, and thus does not restrict the input signal to being
Gaussian or having another distribution. In addition, we propose to adjust
adaptively the intensity parameter of the sparsity attraction term. Finally,
simulation results in sparse system identification demonstrate the
effectiveness of our theoretical results.Comment: 32 pages, 14 figure
Set-membership improved normalized subband adaptive filter algorithms for acoustic echo cancellation
In order to improve the performances of recently-presented improved
normalized subband adaptive filter (INSAF) and proportionate INSAF algorithms
for highly noisy system, this paper proposes their set-membership versions by
exploiting the theory of set-membership filtering. Apart from obtaining smaller
steady-state error, the proposed algorithms significantly reduce the overall
computational complexity. In addition, to further improve the steady-state
performance for the algorithms, their smooth variants are developed by using
the smoothed absolute subband output errors to update the step sizes.
Simulation results in the context of acoustic echo cancellation have
demonstrated the superiority of the proposed algorithms.Comment: 22 pages,8 figures, 3 tables,accepted by IET signal processing on
27-Jul-201