Performance Analysis Of Deficient Length Normalized Lms Algorithm

In the practical applications of adaptive filters, the system to be identified is often modeled as a finite impulse response (FIR) filter. When the a priori information of unknown system is unavailable, the order of transversal adaptive filter is usually less than that of real system impulsive response. However, the deserted weight coefficients of FIR have significant impact on the convergence behavior of the deficient length adaptive filters. This paper studies the performance analysis of deficient length normalized LMS (DL-NLMS) algorithm for the correlated input data, which allows us to further understand its convergence behavior. Simulations illustrate the effectiveness and correctness of the derived theoretical results.