A new estimator for an unknown signal imbedded in additive Gaussian noise
M. Mohajeri · IEEE Transactions on Information Theory · 1974
Estimation of an unknown signal observed in the presence of an additive Gaussian noise process is reduced to the problem of estimating an unknown complex parameter. A new class of estimators for an unknown complex parameter is introduced, and their biases and mean-square errors are studied. The performance of a particular member of this class (c-aestimator) is compared with that of the maximum-likelihood (ML) estimator, and it is shown that thec-aestimator reduces considerably the mean-square error for small values of SNR, at the expense of introducing a small bias. Thec-aand ML estimators of a complex parameter are applied to the problem of signal estimation, and some interesting numerical results are presented.