A technique for pole-zero modeling of complex-valued autocorrelations

Complex-valued time series occur in many applications [1],[2] and frequently it is necessary to obtain a rational model for its autocorrelation function. A computationally efficient, noniterative method for developing such models is presented in this paper. The autocorrelation data samples are processed by a set of complex measurement units (first order recursive filters) to generate a family of auxiliary sequences. The Gram matrix of these sequences and the known impulse response of the measurement units is then shown to yield a parameter vector consisting of the coefficients of an ARMA model. Although similar to [3],[4] the solution is slightly different; also it permits analysis of complex signals. Results of application to SSB coherent radar returns are presented.