scholarly journals Normalized Combinations of Proportionate Affine Projection Sign Subband Adaptive Filter

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Tong An ◽  
Tao Zhang ◽  
Yanzhang Geng ◽  
Haiquan Jiao
Keyword(s):  
Steady State ◽  
Adaptive Filter ◽  
Future Research ◽  
L1 Norm ◽  
Power Normalization ◽  
Affine Projection ◽  
Mixing Parameter ◽  
Subband Adaptive Filter ◽  
Accelerated Gradient ◽  
The Cost

The proportionate affine projection sign subband adaptive filter (PAP-SSAF) has a better performance than the affine projection sign subband adaptive filter (AP-SSAF) when we eliminate the echoes. Still, the robustness of the PAP-SSAF algorithm is insufficient under unknown environmental conditions. Besides, the best balance remains to be found between low steady-state misalignment and fast convergence rate. In order to solve this problem, we propose a normalized combination of PAP-SSAF (NCPAP-SSAF) based on the normalized adaption schema. In this paper, a power normalization adaptive rule for mixing parameters is proposed to further improve the performance of the NCPAP-SSAF algorithm. By using Nesterov’s accelerated gradient (NAG) method, the mixing parameter of the control combination can be obtained with less time consumed when we take the l1-norm of the subband error as the cost function. We also test the algorithmic complexity and memory requirements to illustrate the rationality of our method. In brief, our study contributes a novel adaptive filter algorithm, accelerating the convergence speed, reducing the steady-state error, and improving the robustness. Thus, the proposed method can be utilized to improve the performance of echo cancellation. We will optimize the combination structure and simplify unnecessary calculations to reduce the algorithm’s computational complexity in future research.

scholarly journals Sparse Adaptive Channel Estimation Based onlp-Norm-Penalized Affine Projection Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yingsong Li ◽  
Wenxing Li ◽  
Wenhua Yu ◽  
Jian Wan ◽  
Zhiwei Li
Keyword(s):  
Steady State ◽  
Channel Estimation ◽  
Convergence Speed ◽  
Projection Algorithm ◽  
Broadband Wireless ◽  
Sparse Channel Estimation ◽  
Affine Projection ◽  
Affine Projection Algorithm ◽  
The Cost ◽  
Steady State Performance

We propose anlp-norm-penalized affine projection algorithm (LP-APA) for broadband multipath adaptive channel estimations. The proposed LP-APA is realized by incorporating anlp-norm into the cost function of the conventional affine projection algorithm (APA) to exploit the sparsity property of the broadband wireless multipath channel, by which the convergence speed and steady-state performance of the APA are significantly improved. The implementation of the LP-APA is equivalent to adding a zero attractor to its iterations. The simulation results, which are obtained from a sparse channel estimation, demonstrate that the proposed LP-APA can efficiently improve channel estimation performance in terms of both the convergence speed and steady-state performance when the channel is exactly sparse.

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.

Improved affine projection subband adaptive filter for high background noise environments

2017 ◽  
Vol 137 ◽  
pp. 356-362 ◽  
Author(s):  
Haiquan Zhao ◽  
Zongsheng Zheng ◽  
Zhongyu Wang ◽  
Badong Chen
Keyword(s):  
Adaptive Filter ◽  
Background Noise ◽  
High Background ◽  
Affine Projection ◽  
High Background Noise ◽  
Subband Adaptive Filter

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.

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