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In classical estimation theory, the observation is always assumed to contain the signal to be estimated. In practice, certain observations, or sequences of observations, may contain noise alone, only the probability of occurrence of such cases being available to the estimator. An example is trajectory tracking where the signal is first detected and then the estimator is allowed to process it for tracking purposes. However, any detection decision is associated with a false-alarm probability, which is the probability that the detected signal contains only noise. Minimum mean-square estimators are derived for two different forms of this problem; 1) when it is possible that the observation at any sample time contains signal or is noise alone, independent of the situation at any other sample, and 2) when the entire sequence of observations contains signal or is only noise. The estimators derived are of recursive form. A simple example is given for illustration.