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

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zijie Shen ◽  
Lin Tang ◽  
Li Yang
Keyword(s):  
Steady State ◽  
Cost Function ◽  
Adaptive Filter ◽  
Convex Combination ◽  
Sigmoid Function ◽  
Function Structure ◽  
Step Size ◽  
Subband Adaptive Filter ◽  
Norm Constraint ◽  
Normalized Subband Adaptive Filter

In this paper, by inserting the logarithm cost function of the normalized subband adaptive filter algorithm with the step-size scaler (SSS-NSAF) into the sigmoid function structure, the proposed sigmoid-function-based SSS-NSAF algorithm yields improved robustness against impulsive interferences and lower steady-state error. In order to identify sparse impulse response further, a series of sparsity-aware algorithms, including the sigmoid L0 norm constraint SSS-NSAF (SL0-SSS-NSAF), sigmoid step-size scaler improved proportionate NSAF (S-SSS-IPNSAF), and sigmoid L0 norm constraint step-size scaler improved proportionate NSAF (SL0-SSS-IPNSAF), is derived by inserting the logarithm cost function into the sigmoid function structure as well as the L0 norm of the weight coefficient vector to act as a new cost function. Since the use of the fix step size in the proposed SL0-SSS-IPNSAF algorithm, it needs to make a trade-off between fast convergence rate and low steady-state error. Thus, the convex combination version of the SL0-SSS-IPNSAF (CSL0-SSS-IPNSAF) algorithm is proposed. Simulations in acoustic echo cancellation (AEC) scenario have justified the improved performance of these proposed algorithms in impulsive interference environments and even in the impulsive interference-free condition.

scholarly journals A Variable Step Size for Acoustic Echo Cancellation Using Normalized Sub band Adaptive Filter

2013 ◽  
Vol 6 (3) ◽  
pp. 449-455
Author(s):  
Harjeet Kaur Ojhla ◽  
Dr. Rajneesh Talwar ◽  
Jyoti Darekar
Keyword(s):  
Steady State ◽  
Convergence Rate ◽  
Adaptive Filter ◽  
Weight Vector ◽  
Variable Step Size ◽  
Mean Square ◽  
Step Size ◽  
Variable Step ◽  
Subband Adaptive Filter ◽  
Normalized Subband Adaptive Filter

Numerous various step size normalized least mean square (VSS-NLMS)Algorithms have been derived to solve the problem of fast convergence rate and low mean square error.Here we find out the ways to control the step size. A normalized subband adaptive filter algorithm uses a fixed and variable step size, which is chosen as a trade-off between the steady-state error and the convergence rate. A variable step size for normalized subband adaptive filter is derived by minimizing the mean-square deviation between the optimal weight vector and the estimated weight vector at each instant of time. The variable step size is presented in terms of error variance. Therefore, we verify thedifferent  algorithmseither they are capable of tracking in stationary and non-stationary environments. The results show good tracking ability and low misalignment of the algorithm in system identification. Parameters are tracking, steady state errors, and misalignment, environment, step size.

scholarly journals Subband Adaptive Filtering withl1-Norm Constraint for Sparse System Identification

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Young-Seok Choi
Keyword(s):  
System Identification ◽  
Cost Function ◽  
Sparse System ◽  
Gradient Based ◽  
Subband Adaptive Filter ◽  
Sparse System Identification ◽  
Norm Constraint ◽  
Optimum Solution ◽  
The Cost ◽  
Normalized Subband Adaptive Filter

This paper presents a new approach of the normalized subband adaptive filter (NSAF) which directly exploits the sparsity condition of an underlying system for sparse system identification. The proposed NSAF integrates a weightedl1-norm constraint into the cost function of the NSAF algorithm. To get the optimum solution of the weightedl1-norm regularized cost function, a subgradient calculus is employed, resulting in a stochastic gradient based update recursion of the weightedl1-norm regularized NSAF. The choice of distinct weightedl1-norm regularization leads to two versions of thel1-norm regularized NSAF. Numerical results clearly indicate the superior convergence of thel1-norm regularized NSAFs over the classical NSAF especially when identifying a sparse system.

Sign in / Sign up

Export Citation Format

Share Document