Using recursive least square filter
Unlike a standard high pass filter where you set a cut-off frequency and other design parameters for a fixed filter result with a pass band ripple, stop band rejection, phase response etc.. the "recursive least squares filter" is an adaptive filter commonly used for channel equalization.

As a high level working example of a LMS equalizer, please see my post here where I derived and used the Wiener-Hopf equations to solve for the LMS filter coefficients. This would be an approach to equalization when the coefficients are solved for a block of data and then applied; most applicable to fixed filtering solutions such as compensating for distortions introduced in the RF portion of a receiver (compensating for analog filters for example).

Compensating Loudspeaker frequency response in an audio signal

To adaptively solve for the same coefficients, such as compensating for changing channel effects, two common algorithms are referred to as "LMS: Least-Mean Squared" and "RLS: Recursive Least Squares". I compare the two in my slide copied below, and for more details please refer to the following references

Rappaport, T.S. Wireless Communications Principles and Practice, Second Edition, Prentice Hall 2002

Proakis, J.  “Adaptive Equalization for TDMA Digital Mobile Radio,“ IEEE Transactions on Vehicular Technology, Vol. 40, No. 2 pp 333-341, May 1991

Sayed, Ali, Fundamentals of Adaptive Filtering, Wiley, 2003

Bingham, J.A.C., The Theory and Practice of Modem Design, 1988



The comparison of the implementation for the LMS and RLS adaptive equalizer is shown in the figures below, where vectors are indicated by single bars and matrices with double bars:

LMS: Easy!



RLS: Fast!



Finally to note that a least-squares equalizing filter is NOT the best choice for a channel with severe frequency selective fading as noise enhancement will result where deep fading nulls occur. For this condition, a decision feedback equalizer is often a better choice if time-domain equalization is to be used.