Abstract: We present a family of data-reusing and affine
projection algorithms. For identification of a noisy linear finite
impulse response channel, a partial knowledge of a channel,
especially noise, can be used to improve the performance of
the adaptive filter. Motivated by this fact, the proposed scheme
incorporates an estimate of a knowledge of noise. A constraint, called
the adaptive noise constraint, estimates an unknown information of
noise. By imposing this constraint on a cost function of data-reusing
and affine projection algorithms, a cost function based on the adaptive
noise constraint and Lagrange multiplier is defined. Minimizing the
new cost function leads to the adaptive noise constrained (ANC)
data-reusing and affine projection algorithms. Experimental results
comparing the proposed schemes to standard data-reusing and affine
projection algorithms clearly indicate their superior performance.
Abstract: We propose two affine projection algorithms (APA)
with variable regularization parameter. The proposed algorithms
dynamically update the regularization parameter that is fixed in the
conventional regularized APA (R-APA) using a gradient descent
based approach. By introducing the normalized gradient, the proposed
algorithms give birth to an efficient and a robust update scheme for
the regularization parameter. Through experiments we demonstrate
that the proposed algorithms outperform conventional R-APA in
terms of the convergence rate and the misadjustment error.
Abstract: This work presents a new type of the affine projection
(AP) algorithms which incorporate the sparsity condition of a
system. To exploit the sparsity of the system, a weighted l1-norm
regularization is imposed on the cost function of the AP algorithm.
Minimizing the cost function with a subgradient calculus and
choosing two distinct weighting for l1-norm, two stochastic gradient
based sparsity regularized AP (SR-AP) algorithms are developed.
Experimental results exhibit that the SR-AP algorithms outperform
the typical AP counterparts for identifying sparse systems.
Abstract: This paper proposes a complementary combination scheme of affine projection algorithm (APA) filters with different order of input regressors. A convex combination provides an interesting way to keep the advantage of APA having different order of input regressors. Consequently, a novel APA which has the rapid convergence and the reduced steady-state error is derived. Experimental results show the good properties of the proposed algorithm.
Abstract: This paper suggests a new Affine Projection (AP) algorithm with variable data-reuse factor using the condition number as a decision factor. To reduce computational burden, we adopt a recently reported technique which estimates the condition number of an input data matrix. Several simulations show that the new algorithm has better performance than that of the conventional AP algorithm.
Abstract: Employing a recently introduced unified adaptive filter
theory, we show how the performance of a large number of important
adaptive filter algorithms can be predicted within a general framework
in nonstationary environment. This approach is based on energy conservation
arguments and does not need to assume a Gaussian or white
distribution for the regressors. This general performance analysis can
be used to evaluate the mean square performance of the Least Mean
Square (LMS) algorithm, its normalized version (NLMS), the family
of Affine Projection Algorithms (APA), the Recursive Least Squares
(RLS), the Data-Reusing LMS (DR-LMS), its normalized version
(NDR-LMS), the Block Least Mean Squares (BLMS), the Block
Normalized LMS (BNLMS), the Transform Domain Adaptive Filters
(TDAF) and the Subband Adaptive Filters (SAF) in nonstationary
environment. Also, we establish the general expressions for the
steady-state excess mean square in this environment for all these
adaptive algorithms. Finally, we demonstrate through simulations that
these results are useful in predicting the adaptive filter performance.
Abstract: In this paper, a new pseudo affine projection (AP)
algorithm based on Gauss-Seidel (GS) iterations is proposed for
acoustic echo cancellation (AEC). It is shown that the algorithm is
robust against near-end signal variations (including double-talk).
Abstract: This paper introduces a new variable step-size APA with decorrelation of AR input process is based on the MSD analysis. To achieve a fast convergence rate and a small steady-state estimation error, he proposed algorithm uses variable step size that is determined by minimising the MSD. In addition, experimental results show that the proposed algorithm is achieved better performance than the other algorithms.
Abstract: This paper presents a forgetting factor scheme for variable step-size affine projection algorithms (APA). The proposed scheme uses a forgetting processed input matrix as the projection matrix of pseudo-inverse to estimate system deviation. This method introduces temporal weights into the projection matrix, which is typically a better model of the real error's behavior than homogeneous temporal weights. The regularization overcomes the ill-conditioning introduced by both the forgetting process and the increasing size of the input matrix. This algorithm is tested by independent trials with coloured input signals and various parameter combinations. Results show that the proposed algorithm is superior in terms of convergence rate and misadjustment compared to existing algorithms. As a special case, a variable step size NLMS with forgetting factor is also presented in this paper.