Conventional RLS Adaptive Filter
Least-squares algorithms aim at the minimization of the sum of the squares of the difference between the desired signal and the model filter output –. When new samples of the incoming signals are received at every iteration, the solution for the least-squares problem can be computed in recursive form resulting in the recursive least-squares (RLS) algorithms. The conventional version of these algorithms will be the topic of the present chapter.
KeywordsInput Signal Adaptive Filter Autocorrelation Matrix Optimal Coefficient Eigenvalue Spread
Unable to display preview. Download preview PDF.
- 2.S. Haykin, Adaptive Filter Theory, Prentice Hall, Englewood Cliffs, NJ, 4th edition, 2002.Google Scholar
- 4.J. M. Ciofß, “Limited precision effects in adaptive filtering,” IEEE Trans, on Circuits and Systems, vol. CAS-34, pp. 821–833, July 1987.Google Scholar
- 5.R. S. Medaugh and L. J. Griffiths, “A comparison of two linear predictors,” Proc. IEEE Intern. Conf. on Acoust., Speech, Signal Processing, Atlanta, GA, pp. 293–296, April 1981.Google Scholar
- 6.F. Ling and J. G. Proakis, “Nonstationary learning characteristics of least squares adaptive estimation algorithms,” Proc. IEEE Intern. Conf. on Acoust., Speech, Signal Processing, San Diego, CA, pp. 30.3.1.–30.3.4, March 1984.Google Scholar
- 10.C. R. Johnson, Jr., Lectures on Adaptive Parameter Estimation, Prentice Hall, Englewood Cliffs, NJ, 1988.Google Scholar