A novel gradient adaptive step size LMS algorithm with dual adaptive filters.

Area of Science:

• Biomedical Engineering
• Signal Processing
• Adaptive Filtering

Background:

• Least Mean Square (LMS) adaptive filters are crucial for extracting biomedical signals from noise.
• Fixed-step LMS filters exhibit slow convergence or signal distortion in diverse environments.
• Adaptive filtering systems require environmental adaptability and fast convergence with low distortion.

Purpose of the Study:

• To introduce a novel gradient adaptive step size LMS adaptive filter.
• To address the limitations of fixed-step LMS filters in biomedical applications.
• To achieve improved adaptation and performance in noisy biomedical signal extraction.

Main Methods:

• A gradient adaptive step size LMS adaptive filter is proposed.
• The algorithm employs two adaptive filters for accurate gradient estimation.
• Computational complexity is maintained low despite using two LMS filters.

Main Results:

• The proposed algorithm demonstrates good adaptation and performance.
• Accurate gradient estimation is achieved through the dual-filter approach.
• Simulations in an active noise cancellation system for heartbeat and lung sound extraction validate the performance.

Conclusions:

• The novel gradient adaptive step size LMS filter offers superior performance compared to fixed-step LMS.
• The dual-filter approach provides accurate gradient estimation with low computational cost.
• This method is effective for extracting vital biomedical signals in challenging noisy conditions.