Simplified design of low-delay oversampled NPR GDFT filterbanks
EURASIP Journal on Applied Signal Processing
Design of low-delay nonuniform oversampled filterbanks
Signal Processing
A new class of invertible FIR filters for spectral shaping
Signal Processing
Frequency-selective KYP lemma, IIR filter, and filter bank design
IEEE Transactions on Signal Processing
Optimization of the higher density discrete wavelet transform and of its dual tree
IEEE Transactions on Signal Processing
| Hi-index | 35.69 |
An algorithm for moving average (MA) parameter estimation was proposed by Stoica et al. (see ibid. vol.48, p.1999-2012, 2000). Its key step (covariance fitting) is a semidefinite programming (SDP) problem with two convex constraints: one reflecting the real positiveness of the desired covariance sequence and the other having a second-order cone form. We analyze two parameterizations of a positive real sequence and show that there is a one-to-one correspondence between them. We also show that the dual of the covariance fitting problem has a significantly smaller number of variables and, thus, a much reduced computational complexity. We discuss in detail the formulations that are best suited for the currently available semidefinite quadratic programming packages. Experimental results show that the execution times of the newly proposed algorithms scale well with the MA order, which are therefore convenient for large-order MA signals