A Variable Step-Size Normalized Subband Adaptive Filter With a Step-Size Scaler Against Impulsive Measurement Noise

Block‐sparse non‐uniform norm constraint normalised subband adaptive filter

“…An AR(1) signal with a pole at 0.9 is used as the input signal. As the measurement noise, the Gaussian noise is added to the desired signal with a signal‐to‐noise ratio (SNR) of 30 dB [7]. The cosine‐modulated filter banks whose subband numbers are set to 2, 4 or 8 are utilised for the subband structure.…”

“…Due to this advantage, it has been widely used in acoustic echo cancelation to create a precise replica of the loudspeaker-enclosure-microphone. Following this direction, various works to enhance the performance of NSAF have been conducted in [3][4][5][6][7][8][9][10][11][12][13].…”

Robust Normalized Subband Adaptive Filter Algorithm with a Sigmoid-Function-Based Step-Size Scaler and Its Convex Combination Version

“…For the purpose of decreasing steady-state error and speeding up the convergence rate of the SSAF algorithm, variable regularization parameter SSAF (VRP-SSAF) [12], some variable step-size SSAF algorithms [14,15], and affine projection SSAF [16,17] have been proposed. Nowadays many researchers have demonstrated that making full use of the saturation property of the error nonlinearities can gain splendid robustness against impulsive interferences, such as normalized logarithmic SAF (NLSAF) [18], arctangentbased NSAF algorithms (Arc-NSAFs) [19], maximum correntropy criterion (MCC) [20], the adaptive algorithms based on the step-size scaler (SSS) [21,22], and based on sigmoid function [23,24], and M-estimate based subband adaptive filter algorithm [25].…”

“…In this paper, by inserting the logarithm cost function of the normalized subband adaptive filter algorithm with the step-size scaler (SSS-NSAF) [22] into the sigmoid function structure, the proposed sigmoid-function-based SSS-NSAF (S-SSS-NSAF) algorithm yields improved robustness against impulsive interferences and lowers steady-state error. In order to identify sparse impulse response further, a series of sparsity-aware algorithms, including the sigmoid L 0 norm constraint SSS-NSAF (SL 0 -SSS-NSAF), sigmoid step-size scaler improved proportionate NSAF (S-SSS-IPNSAF), and sigmoid L 0 norm constraint improved proportionate NSAF (SL 0 -SSS-IPNSAF), are derived by inserting the logarithm cost function into the sigmoid function structure as well as the L 0 norm of the weight coefficient vector to act as a new cost function.…”

DCD-Based Recursive Adaptive Algorithms Robust Against Impulsive Noise

“…Taking advantage of the Geman-McClure (GMC) estimator, a recursive algorithm [12] for Volterra system identification was derived, which shows a better performance than RLpN and RLM algorithms in impulsive noise modeled by the α-stable distribution [7]. When impulsive noise appears, by incorporating the step-size scaler into the update term, a robust subband algorithm was developed [13]. The correntropy measures the similarity between two variables, which is helpful for suppressing large outliers; thus, the maximum correntropy criterion (MCC) has been used for improving the anti-jamming capability of adaptive filters to impulsive noise, yielding the GD-based MCC [14]- [16] and recursive MCC (RMCC) algorithms [17], [18].…”