An LS based new gradient type adaptive algorithm-least squares gradient

The proposed algorithm updates filter coefficients using the time averaged gradient vectors and it ensures the good performance under noisy conditions and under finite precision implementation. It is shown that we can update the filter coefficients as many as the algorithm requires to converge even if only short data are available so that the proposed algorithm can converge faster than other gradient type algorithms, i.e. the LMS algorithm. Moreover, LMS algorithm can be regarded as a special case of the proposed algorithm and the tracking ability of the algorithm is shown