The maximum likelihood estimator (MLE) is widely used in practical signal modeling . A full treatment of maximum likelihood estimators (and statistical estimators in general) lies beyond the scope of this book. However, we will show that the MLE is equivalent to the least squares estimator for a wide class of problems, including well resolved sinusoids in white noise.
Consider again the signal model of (5.32) consisting of a complex sinusoid in additive white (complex) noise:
We express the zero-mean Gaussian assumption by writing
It turns out that when Gaussian random variables are uncorrelated (i.e., when is white noise), they are also independent. This means that the probability of observing particular values of and is given by the product of their respective probabilities . We will now use this fact to compute an explicit probability for observing any data sequence in (5.44).
Since the sinusoidal part of our signal model, , is deterministic; i.e., it does not including any random components; it may be treated as the time-varying mean of a Gaussian random process . That is, our signal model (5.44) can be rewritten as