Steady-state mean-square deviation analysis of improved normalized subband adaptive filter

2015 ◽  
Vol 106 ◽  
pp. 49-54 ◽  
Author(s):  
Jae Jin Jeong ◽  
Keunhwi Koo ◽  
Gyogwon Koo ◽  
Sang Woo Kim
2013 ◽  
Vol 6 (3) ◽  
pp. 449-455
Author(s):  
Harjeet Kaur Ojhla ◽  
Dr. Rajneesh Talwar ◽  
Jyoti Darekar

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.


2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zijie Shen ◽  
Lin Tang ◽  
Li Yang

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.


IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 218793-218802
Author(s):  
Minho Lee ◽  
Taesu Park ◽  
Junwoong Hur ◽  
Poogyeon Park

Sign in / Sign up

Export Citation Format

Share Document