Correlation Function Estimator Performance in Non-Gaussian Spherically Invariant Random Processes
ROME LAB ROME NY
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In this report, analytic expressions are developed for the variance, error variance and bias of the time-averaged correlation function estimator for stationary, discrete, non-Gaussian complex processes. The expressions derived here pertain to the general class of non-Gaussian processes known as Spherically Invariant Random Processes SIRPs Specific results are shown for K-distributed processes which form a special case of the SIRPs. Furthermore, these equations are derived for the general case of processes with unconstrained quadrature components i.e., for processes exhibiting elliptical symmetry For the special case of complex processes with constrained correlation between the quadrature components i.e., circular symmetry, the resulting analytic expressions attain a simplified form. Validity of the analytic expressions is presented using Monte-Carlo simulations. Correlation function estimator, Estimation, Spherically invariant random processes, Ergodicity, Non-Gaussian random processes.
- Theoretical Mathematics