Recursive Least Squares (RLS) is an adaptive filtering algorithm that minimizes the weighted sum of the squares of the differences between the desired and actual output over time. It updates filter coefficients recursively as new data arrives, making it efficient for real-time applications. This adaptability and efficiency allow RLS to perform well in environments where the signal characteristics can change rapidly, connecting it closely with various algorithms and structures used for signal processing and beamforming.
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