RLS Linear Array Algorithm Based on Recursive Principle

The RLS (Recursive Least Squares) linear array algorithm based on recursive principles is a widely used signal processing algorithm. It can be applied to filtering, noise reduction, and other domains with extensive practical applications. The algorithm typically implements recursive weight updates using a forgetting factor to track signal variations in real-time. Key implementation components often include covariance matrix inversion using the matrix inversion lemma for computational efficiency.

If you want to study this algorithm in depth, you may refer to the following resources:

- Fundamental principles and applications of RLS linear array algorithms (covering weight vector update equations and convergence analysis)

- Speech processing techniques using RLS linear array algorithms (demonstrating adaptive filter implementation for echo cancellation)

- Applications of RLS linear array algorithms in radar signal processing (highlighting DOA estimation and beamforming code examples)

These resources are beginner-friendly and particularly suitable for introductory learning. Hope this helps your study!