Search

The normalized subband adaptive filtering algorithm provides fast convergence rate for colored input signals as compared to the normalized least mean square algorithm, but it suffers from a poor convergence issue in the α -stable noise. In light of this, the normalized subband p-norm (NSPN) algorithm, which is based on the least mean p-power error (MPE) criterion, is proposed in this study. This technique is not only robust against impulsive noise samples, but it also maintains a fast convergence rate when colored input signals are used. In addition to this, we develop both the steady-state and the transient models of the NSPN algorithm and provide some insights. Then, in order to solve the problem of making a choice regarding the order p in the NSPN algorithm, we design an autonomous system and come up with the NSPN algorithm with a variable p-norm (VP-NSPN). In addition, we offer the TFC-based VP-NSPN algorithm with a fast convergence rate and low steady-state misadjustment simultaneously by making use of the tap-weights feedback-based convex combination (TFC) scheme. In conclusion, simulation results on system identification and acoustic echo cancellation are undertaken in order to validate the superiority of the proposed algorithms and validate the usefulness of the theoretical analysis. [ABSTRACT FROM AUTHOR]

In this paper, two arctangent-based normalized subband adaptive filter (Arc-NSAF) algorithms are proposed, which are derived from two different forms of arctangent cost function and their difference is the order of the arctangent computation and the summation operation of the normalized subband error signal. Benefiting from the excellent property of the arctangent function for suppressing impulsive interferences, the proposed Arc-NSAF algorithms not only possess good robustness against impulsive interferences, but also obtain faster convergence rate for the colored input signals. To further improve the convergence performance of the Arc-NSAFs when identifying sparse system, the proportionate versions of the Arc-NSAF algorithms are also proposed. Simulation results have demonstrated the superiority of the proposed algorithms in impulsive interferences environments as compared to their respective competitors. [ABSTRACT FROM AUTHOR]

A novel normalized subband adaptive filter algorithm with combined step size is proposed for acoustic echo cancellation, which is derived by utilizing a variable mixing parameter to combine a large step size and a small one, thus providing fast convergence rate and small steady-state error. The mixing parameter is indirectly updated by utilizing the stochastic gradient method which minimizes the sum of squared subband errors. Simulation results demonstrate the superiority of the proposed algorithm in terms of the convergence rate and steady-state error as compared to other algorithms mentioned in this paper. [ABSTRACT FROM AUTHOR]

In acoustic echo cancellation (AEC), the sparseness of impulse responses can vary over time or/and context. For such scenario, the proportionate normalized subband adaptive filter (PNSAF) and μ -law (MPNSAF) algorithms suffer from performance deterioration. To this end, we propose their sparseness-measured versions by incorporating the estimated sparseness into the PNSAF and MPNSAF algorithms, respectively, which can adapt to the sparseness variation of impulse responses. In addition, based on the energy conservation argument, we provide a unified formula to predict the steady-state mean-square performance of any PNSAF algorithm, which is also supported by simulations. Simulation results in AEC have shown that the proposed algorithms not only exhibit faster convergence rate than their competitors in sparse, quasi-sparse and dispersive environments, but also are robust to the variation in the sparseness of impulse responses. [ABSTRACT FROM AUTHOR]

Proportionate-type normalized subband 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. [ABSTRACT FROM AUTHOR]

• To analyze the performance of three proportionate algorithms in impulsive noises. • To reveal the ranges and relations of the step-sizes in three proportionate algorithms. • To develop a decorrelation technique based variable step-size scheme to deal with the trade-off between convergence rate and steady-state error. In the literature, the proportionate least mean M-estimate (PLMM) algorithm exhibits good performance when dealing with sparse systems in the presence of impulsive noises. In this paper, the mean and mean-square behaviors of three recursion types of the PLMM algorithm are studied in depth. We derive analytically the stability, transient and steady-state results of these PLMM recursions, and find that they can achieve the same performance when properly choosing the step-size and the proportionate matrix. To improve the filter performance in both convergence rate and steady-state error, we derive a variable step-size (VSS) scheme and then present the VSS-based PLMM algorithms. In addition, based on the adaptive decorrelation strategy aiming at the colored input signals, the VSS adaptive decorrelation PLMM algorithms are developed to further speed up the convergence. Computer simulations have verified our theoretical analyses and the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a new sign subband adaptive filter (SSAF) algorithm with an individual-weighting-factor (IWF) for each subband, instead of a common weighting factor in the original SSAF algorithm, called the IWF-SSAF. Each individual weighting factor only depends on the corresponding subband input signal power. Compared with the SSAF algorithm, the proposed approach fully utilizes the inherent decorrelating property of subband adaptive filter for colored inputs, leading to a better convergence performance. After that, to further enhance the performance of the IWF-SSAF in a sparse system, an improved proportionate IWF-SSAF (IWF-IPSSAF) algorithm is proposed. The proposed algorithms not only inherit the good robustness of sign algorithm against impulsive interferences, but also obtain a significant improvement in the performance as compared to their counterparts (i.e., SSAF and IPSSAF), in terms of the convergence rate and tracking capability. Besides, the IWF-IPSSAF algorithm has faster convergence rate than the IWF-SSAF algorithm for sparse impulse responses. Finally, the performances of two proposed algorithms are demonstrated in the system identification and the acoustic echo cancellation with double-talk. [ABSTRACT FROM AUTHOR]

Proposed is a new subband adaptive filter (SAF) algorithm by minimizing Huber’s cost function that is robust to impulsive noises. Generally, this algorithm works in the mode of the normalized SAF (NSAF) algorithm, while it behaves like the sign SAF (SSAF) algorithm only when the impulsive noises appear. To further improve the robustness of this algorithm against impulsive noises, the subband cutoff parameters are updated in a recursive way. Moreover, the proposed algorithm can be interpreted as a variable step size NSAF algorithm, thus it exhibits faster convergence rate and lower steady-state error than the NSAF. Simulation results, using different colored input signals in both impulsive and free-impulsive noise environments, show that the proposed algorithm works better than some existing algorithms. [ABSTRACT FROM AUTHOR]

• By incorporating the sparsity-aware technique, we propose the S-IWF-SSAF algorithm, and analyze its performance in-depth in impulsive noise. • The proposed analysis covers the behaviors of the IWF-SSAF algorithm in impulsive noise, with better accuracy than the analysis in [15]. • We devise a joint optimization scheme to automatically choose the step-size and the sparsity penalty parameter for the S-IWF-SSAF algorithm. • To make the proposed algorithms suitable for AEC, we develop delayless implementation of the proposed algorithms. In this paper, we propose and analyze the sparsity-aware sign subband adaptive filtering with individual weighting factors (S-IWF-SSAF) algorithm, and consider its application in acoustic echo cancellation (AEC). Furthermore, we design a joint optimization scheme of the step-size and the sparsity penalty parameter to enhance the S-IWF-SSAF performance in terms of convergence rate and steady-state error. A theoretical analysis shows that the S-IWF-SSAF algorithm outperforms the previous sign subband adaptive filtering with individual weighting factors (IWF-SSAF) algorithm in sparse scenarios. In particular, compared with the existing analysis on the IWF-SSAF algorithm, the proposed analysis does not require the assumptions of large number of subbands, long adaptive filter, and paraunitary analysis filter bank, and matches well the simulated results. Simulations in both system identification and AEC situations have demonstrated our theoretical analysis and the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a new proportionate normalized subband adaptive filter (PNSAF) algorithm, called the IAF-PNSAF, which assigns an individual activation factor for each filter coefficient instead of a common one for all filter coefficients as in the standard PNSAF algorithm. This algorithm achieves an improved performance in terms of the convergence rate and tracking capability for identifying a highly sparse system. Moreover, to overcome the trade-off of the fixed-step-size IAF-PNSAF algorithm between the fast convergence rate and small steady-state misalignment, its adaptive combination version is proposed. Simulation results for identifying the impulse response with high sparseness have demonstrated that the proposed algorithms outperform their counterparts. [ABSTRACT FROM AUTHOR]

• This paper provides the robust SAF-LHC-MNAG algorithm for nonlinear system identification in impulsive noise scenarios. • The proposed algorithm can accelerate the convergence under the premise of low steady-state error. • The convergence condition and the mean square performance of the SAF-LHC-MNAG algorithm are given. • The proposed algorithm is implemented to identify the hysteresis model of the piezoelectric actuator. This paper proposes a novel spline adaptive filtering (SAF) algorithm for nonlinear system identification under impulsive noise environments. This algorithm combines the logarithmic hyperbolic cosine (LHC) cost function and the modified Nesterov accelerated gradient (MNAG) learning method, which is called the SAF-LHC-MNAG algorithm. The LHC cost function can reduce the sensitivity of SAF to large outliers and improve the robustness to impulsive noises. Additionally, the MNAG method can further accelerate the convergence under the premise of low steady-state error. Performance analysis of this algorithm is carried out and supported by simulations. Numerical results show that the SAF-LHC-MNAG algorithm has better convergence performance than some existing SAF algorithms. Besides, experimental results confirm the effectiveness of SAF-LHC-MNAG for the accurate identification of nonlinear hysteresis system. [ABSTRACT FROM AUTHOR]

Highlights • This paper provides the development of the robust censored adaptive algorithm for the impulsive noise. • This paper gives Consideration of non-Gaussian background noise as different from previous literature which only consider the Gaussian noise. • Theoretical insights into the mean and mean square performance of the CR-MCC algorithm are provided. • Simulation examples to demonstrate the performance of the proposed algorithm in impulsive noise scenarios are given. Abstract Censored observations and impulsive measurement noise are encountered in many practical applications of adaptive signal processing. Traditional adaptive filtering algorithms may fail to work in such cases. This paper proposes a robust adaptive filter algorithm predicated on maximum correntropy criteria (MCC) for censored regression. A detailed performance analysis in terms of mean and mean-square behaviour is provided. Simulations with Gaussian and non-Gaussian noise are presented to verify the theoretical results, and to demonstrate the superior performance of the proposed algorithm over existing algorithms. [ABSTRACT FROM AUTHOR]