  This paper investigates the problem of estimating the spectral power
parameters of random analog sources using numerical measurements acquired with
minimum digitization complexity. Therefore, spectral analysis has to be
performed with binary samples of the analog sensor output. Under the assumption
that the structure of the spectral power density of the analog sources is
given, we investigate the achievable accuracy for power level estimation with
likelihood-oriented processing. The discussion addresses the intractability of
the likelihood with multivariate hard-limited samples by exploiting advances on
probabilistic modeling and statistical processing of hard-limited multivariate
Gaussian data. In addition to estimation-theoretic performance analysis, the
results are verified by running an iterative likelihood-oriented algorithm with
synthetic binary data for two exemplary sensing setups.
