Robust adaptive filtering algorithms for system identification and array signal processing in non-Gaussian environment

This dissertation proposes four new algorithms based on fractionally lower order statistics for adaptive filtering in a non-Gaussian interference environment. One is the affine projection sign algorithm (APSA) based on L₁ norm minimization, which combines the ability of decorrelating colored input and suppressing divergence when an outlier occurs. The second one is the variable-step-size normalized sign algorithm (VSS-NSA), which adjusts its step size automatically by matching the L₁ norm of the a posteriori error to that of noise. The third one adopts the same variable-step-size scheme but extends L₁ minimization to Lp minimization and the variable step-size normalized fractionally lower-order moment (VSS-NFLOM) algorithms are generalized. Instead of variable step size, the variable order is another trial to facilitate adaptive algorithms where no a priori statistics are available, which leads to the variable-order least mean pth norm (VO-LMP) algorithm, as the fourth one. These algorithms are applied to system identification for impulsive interference suppression, echo cancelation, and noise reduction. They are also applied to a phased array radar system with space-time adaptive processing (beamforming) to combat heavy-tailed non-Gaussian clutters. The proposed algorithms are tested by extensive computer simulations. The results demonstrate significant performance improvements in terms of convergence rate, steady-state error, computational simplicity, and robustness against impulsive noise and interference --Abstract, page iv

An affine combination of two LMS adaptive filters - Transient mean-square analysis

This paper studies the statistical behavior of an affine combination of the outputs of two LMS adaptive filters that simultaneously adapt using the same white Gaussian inputs. The purpose of the combination is to obtain an LMS adaptive filter with fast convergence and small steady-state mean-square deviation (MSD). The linear combination studied is a generalization of the convex combination, in which the combination factor $\lambda(n)$ is restricted to the interval $(0,1)$. The viewpoint is taken that each of the two filters produces dependent estimates of the unknown channel. Thus, there exists a sequence of optimal affine combining coefficients which minimizes the MSE. First, the optimal unrealizable affine combiner is studied and provides the best possible performance for this class. Then two new schemes are proposed for practical applications. The mean-square performances are analyzed and validated by Monte Carlo simulations. With proper design, the two practical schemes yield an overall MSD that is usually less than the MSD's of either filter

Analysis of the normalized LMS optimum solution in the context of channel equalization

Albeit being presented as an alternative to the classical least-mean-square (LMS) algorithm, the normalized LMS (NLMS) actually deals with a modified mean squared error (MSE) cost function, so that the expected optimum solution may differ from the Wiener solution. In this work, we perform an investigation concerning the question as to whether such difference may arise in the context of the channel equalization problem by considering a representative set of transmitted signal modulations, channel models and signal-to-noise ratio (SNR) conditions. Additionally, we analyze the influence of the potential deviation from the optimal solution on the performance of the equalizer3312230CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQnão te

Adaptive filtering algorithms for noise cancellation

Tese de mestrado. Mestrado Integrado em Engenharia Electrotécnica e de Computadores - Major Automação. Faculdade de Engenharia. Universidade do Porto. 201

Robust Reduced-Rank Adaptive Processing Based on Parallel Subgradient Projection and Krylov Subspace Techniques

In this paper, we propose a novel reduced-rank adaptive filtering algorithm by blending the idea of the Krylov subspace methods with the set-theoretic adaptive filtering framework. Unlike the existing Krylov-subspace-based reduced-rank methods, the proposed algorithm tracks the optimal point in the sense of minimizing the \sinq{true} mean square error (MSE) in the Krylov subspace, even when the estimated statistics become erroneous (e.g., due to sudden changes of environments). Therefore, compared with those existing methods, the proposed algorithm is more suited to adaptive filtering applications. The algorithm is analyzed based on a modified version of the adaptive projected subgradient method (APSM). Numerical examples demonstrate that the proposed algorithm enjoys better tracking performance than the existing methods for the interference suppression problem in code-division multiple-access (CDMA) systems as well as for simple system identification problems.Comment: 10 figures. In IEEE Transactions on Signal Processing, 201