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A new Lp-norm constraint least mean square (Lp-LMS) algorithm with newstrategy of varying p is presented, which is applied to system identificationin this letter. The parameter p is iteratively adjusted by the gradient methodapplied to the root relative deviation of the estimated weight vector.Numerical simulations show that this new algorithm achieves lower steady-stateerror as well as equally fast convergence compared with the traditional Lp-LMSand LMS algorithms in the application setting of sparse system identificationin the presence of noise.

Variable p norm constrained LMS algorithm based on gradient of root relative deviation.pdf