Gradient adaptive filters are powerful algorithms used to adjust the coefficients of a filter in response to changing signal conditions. By utilizing a gradient descent approach, they iteratively update the filter coefficients to minimize an error metric, such as the mean squared error. This allows the filter to dynamically adapt to time-varying or unknown system characteristics, making it highly effective in applications like noise cancellation, channel equalization, and system identification.
