Showing papers by "Thomas L. Marzetta published in 1997"

Time-varying weight estimation

Abstract: An apparatus for performance improvement of a digital wireless receiver comprises a processing circuit for processing a plurality of received signals and providing a processed signal, wherein a plurality of weights is applied to the plurality of received signals producing a plurality of weighted received signals. The plurality of weighted received signals are combined to provide the processed signal. A weight generation circuit generates the plurality of weights, wherein weights are generated so as to solve a minimization problem that penalizes excessive time variability in the weights.

Computing the Barankin bound, by solving an unconstrained quadratic optimization problem

Abstract: The Barankin (1949) bound is the greatest lower bound on the variance of any unbiased estimate for a nonrandom parameter. Computing this bound yields, as a byproduct, an unbiased estimator that is at least locally best in the following sense. The estimator formula contains a reference parameter, when the unknown parameter happens to be equal to the reference, the variance of the estimate achieves the Barankin bound. If the dependence of the Barankin estimate on the reference parameter vanishes, then the estimate is also uniformly minimum variance. We obtain a simple derivation of the Barankin bound as the solution of an unconstrained convex quadratic optimization problem. In contrast the standard form of the Barankin bound involves the maximization of a ratio of quadratic quantities. For the case of PET inversion and natural gamma ray spectrometry, the Barankin estimate is only locally minimum variance, but it can be a viable alternative to the maximum likelihood estimate.