Adaptive Filtering Code Samples

This repository contains four Python code samples that demonstrate the use of various adaptive filtering algorithms for signal processing and parameter estimation. Each code sample is accompanied by an explanation of its purpose and functionality.

Code Samples

1. Noisy Signal Denoising with LMS and NLMS

• File: noisy_signal_denoising.py
• Description: This code generates a noisy sine wave signal and applies both the Least Mean Squares (LMS) and Normalized LMS (NLMS) adaptive filters to denoise the signal.

2. Linear Parameter Estimation with LMS

• File: linear_parameter_estimation.py
• Description: This code implements the Least Mean Squares (LMS) algorithm to estimate the parameters of a linear model using synthetic data.

3. Recursive Least Squares (RLS) and RLSL for Signal Filtering

• File: rls_and_rlsl_signal_filtering.py
• Description: This code generates a noisy sine wave signal and applies two different adaptive filtering algorithms: Recursive Least Squares (RLS) and Recursive Least Squares with Leakage (RLSL).

4. Recursive Least Squares (RLS) for Linear Parameter Estimation

• File: rls_parameter_estimation.py
• Description: This code generates synthetic data and uses the Recursive Least Squares (RLS) algorithm to estimate the parameters of a linear model.

Prerequisites

To run these code samples, you will need:

• Python (3.6 or higher)
• NumPy
• Matplotlib

Usage

You can run each code sample independently by executing the corresponding Python script. Make sure you have the required libraries installed.

Acknowledgments NumPy Matplotlib Feel free to explore and use these code samples for your own projects or learning purposes.

If you have any questions or feedback, please open an issue on this repository.

Happy coding!

Author

Amirhossein Omidi

Contact

[email protected]