  This paper deals with the estimation of a sequence of frequencies from a
corresponding sequence of signals. This problem arises in fields such as
Doppler imaging where its specificity is twofold. First, only short noisy data
records are available (typically four sample long) and experimental constraints
may cause spectral aliasing so that measurements provide unreliable, ambiguous
information. Second, the frequency sequence is smooth. Here, this information
is accounted for by a Markov model and application of the Bayes rule yields the
a posteriori density. The maximum a postariori is computed by a combination of
Viterbi and descent procedures. One of the major features of the method is that
it is entirely unsupervised. Adjusting the hyperparameters that balance
data-based and prior-based information is done automatically by ML using an
EM-based gradient algorithm. We compared the proposed estimate to a reference
one and found that it performed better: variance was greatly reduced and
tracking was correct, even beyond the Nyquist frequency.
