Synthesis of correlated multichannel random processes

This paper describes a method for synthesizing multichannel autoregressive (AR) random processes. The procedure allows for the variation of temporal and cross-channel correlation subject to specific constraints for correlation functions. The resulting synthesized processes provide a "fit" in a minimum mean squared error (MMSE) sense to the process correlation functions specified in terms of their temporal and cross-channel correlation parameters. Computer simulation results are presented showing the case of a two-channel AR process with various values of temporal and cross-channel correlation. A method is also suggested to synthesize a more general class of Gaussian processes with unconstrained quadrature components.<
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