System identification: theory for the user
System identification: theory for the user
Statistical Digital Signal Processing and Modeling
Statistical Digital Signal Processing and Modeling
Blind wideband source separation
ICASSP '94 Proceedings of the Acoustics, Speech, and Signal Processing,1994. on IEEE International Conference - Volume 04
ARMA model parameter estimation based on the equivalent MA approach
Digital Signal Processing
A new class of invertible FIR filters for spectral shaping
Signal Processing
MA estimation in polynomial time
IEEE Transactions on Signal Processing
An improved inverse filtering method for parametric spectralestimation
IEEE Transactions on Signal Processing
Cepstral coefficients, covariance lags, and pole-zero models forfinite data strings
IEEE Transactions on Signal Processing
On the parameterization of positive real sequences and MA parameterestimation
IEEE Transactions on Signal Processing
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This paper presents a proof for the existence of a class of LTI causal IFIRF converting spectrum of an arbitrary stationary input process to an output with prescribed set of normalized autocorrelation samples. The input is an MA, AR, or ARMA process of finite order, with or without additive white noise, whose stable model may or may not be minimum phase. The prescribed values of finite number of output autocorrelation lags (except lag zero) may or may not all be equal to zero. The FIR filter is minimum phase and of finite order. It is derived from the predefined input and output autocorrelation samples directly using no intermediate filtering stage or minimization of a cost function. The filters' output autocorrelation lags match the prescribed values precisely. Such a filter provides an alternative solution to the problem of finding a causal IFIRF for spectral shaping in statistical signal processing applications.