Apply adaptive filters to signal separation using a structure called an adaptive line enhancer (ALE). In adaptive line enhancement, a measured signal x(n) contains two signals, an unknown signal of interest v(n), and a nearly-periodic noise signal eta(n).

Use an RLS filter to extract useful information from a noisy signal. The information bearing signal is a sine wave that is corrupted by additive white gaussian noise.

Use the Least Mean Square (LMS) algorithm to subtract noise from an input signal. The LMS adaptive filter uses the reference signal on the Input port and the desired signal on the Desired port to automatically match the filter response. As it converges to the correct filter model, the filtered noise is subtracted and the error signal should contain only the original signal.

Use a Filtered-X LMS algorithm in Adaptive Noise Control (ANC).

The convergence path taken by different adaptive filtering algorithms. The plot is a sequence of points of the form (w1,w2) where w1 and w2 are the weights of the adaptive filter. The blue dots in the figure indicate the contour lines of the error surface. Zoom into the graph to see properties of convergence path by selecting Zoom In from the Tools menu.

Subtract noise from an input signal using the Recursive Least Squares (RLS) algorithm. The RLS adaptive filter uses the reference signal on the Input port and the desired signal on the Desired port to automatically match the filter response in the Noise Filter block. As it converges to the correct filter, the filtered noise should be completely subtracted from the "Signal+Noise" signal, and the "Error Signal" should contain only the original signal.

Track the time-varying weights of a nonstationary channel using the Recursive Least Squares (RLS) algorithm.

Adaptively estimate the time delay for a noisy input signal using the LMS adaptive FIR algorithm. The peak in the filter taps vector indicates the time-delay estimate.