Scholarlink

<p>The affine projection sign subband adaptive filter AP-SSAF can be im- proved regarding convergence performance. The proportionate AP-SSAF (PAP-SSAF) introduced a new algorithm in scenarios with highly cor- related inputs. Regarding echo elimination, the PAP-SSAF outperforms the AP-SSAF in performance. The PAP-SSAF algorithm takes advan- tage of decorrelation, a characteristic of SAF, resulting in convergence more rapidly than the method used by AP-SSAF. However, the PAP- SSAF algorithm must be more robust when confronted with unknown en- vironmental conditions. Furthermore, the ideal ratio between the conver- gence speed and the level of steady-state errors must still be determined. This study presents the NCBC-PAP-SSAF method based on the normal- ized adaptation schema and bias compensation to minimize the estimated bias by including an unbiasedness requirement. The goal is to acquire a quick convergence speed and minimal steady-state errors. This research presents a novel combinational structure incorporating four independent PAP-SSAF algorithms based on the normalized adaptation method. The primary goal is to enhance the NCBC-PAP-SSAF algorithm’s convergence performance by using four distinct filters, which allows us to benefit from a small and big step-size filter concurrently. The mixing factor for control combinations can be obtained effectively, reducing computational time when a subband error’s l1-norm is the cost factor. Nesterov’s accelerated gradient (NAG) approach can be used to determine the control combi- nation’s mixing factor in less time. Lastly, mathematical models used in acoustic cancellation of echos and system identification applications show that the NCBC-PAP-SSAF approach performs better than other meth- ods concerning estimating inaccuracy and tracking capabilities. To lower the computational cost of the technique, we will refine the combination structure and eliminate pointless computations in further studies.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A Novel Approach for Bias-Compensated Normalized Combinations of Proportionate Affine Projection Sign Subband Adaptive Filters

  • 期刊论文

  • 发表日期：

    2025年8月7日

查看全文
  • Tao Zhang,
  • Stephen Afrifa,
  • M. D. Sazzad Hossen,
  • S. M. Shaon,
  • Nahid Al Mahmud,
  • Mensah Samuel Yaw,
  • Yanzhang Geng

摘要

The affine projection sign subband adaptive filter AP-SSAF can be im- proved regarding convergence performance. The proportionate AP-SSAF (PAP-SSAF) introduced a new algorithm in scenarios with highly cor- related inputs. Regarding echo elimination, the PAP-SSAF outperforms the AP-SSAF in performance. The PAP-SSAF algorithm takes advan- tage of decorrelation, a characteristic of SAF, resulting in convergence more rapidly than the method used by AP-SSAF. However, the PAP- SSAF algorithm must be more robust when confronted with unknown en- vironmental conditions. Furthermore, the ideal ratio between the conver- gence speed and the level of steady-state errors must still be determined. This study presents the NCBC-PAP-SSAF method based on the normal- ized adaptation schema and bias compensation to minimize the estimated bias by including an unbiasedness requirement. The goal is to acquire a quick convergence speed and minimal steady-state errors. This research presents a novel combinational structure incorporating four independent PAP-SSAF algorithms based on the normalized adaptation method. The primary goal is to enhance the NCBC-PAP-SSAF algorithm’s convergence performance by using four distinct filters, which allows us to benefit from a small and big step-size filter concurrently. The mixing factor for control combinations can be obtained effectively, reducing computational time when a subband error’s l1-norm is the cost factor. Nesterov’s accelerated gradient (NAG) approach can be used to determine the control combi- nation’s mixing factor in less time. Lastly, mathematical models used in acoustic cancellation of echos and system identification applications show that the NCBC-PAP-SSAF approach performs better than other meth- ods concerning estimating inaccuracy and tracking capabilities. To lower the computational cost of the technique, we will refine the combination structure and eliminate pointless computations in further studies.