Several models are proposed which are believed to be generic for the estimation of discrete-time axial processes. By introducing axial exponential Fourier densities and axial exponential trigonometric densities, finite-dimensional recursive schemes are obtained for updating the conditional density functions. The underlying idea is the closure properties under the Bayes rule of the various combinations of these exponential densities. An estimation error criterion is introduced which is compatible with a Riemannian metric. The corresponding Optimal axial estimates be easily computed from the conditional probability distributions.

Two classes of probability densities, the exponential Fourier densities and the exponential trigonometric densities, are introduced on the unit sphere, as well as four kinds of displacements. In general, neither class is closed under the operation of taking conditional distributions with respect to any of the displacements. A combined usage of both classes is required to study the estimation and detection models obtained from various combinations of the displacements. The merits and disadvantages of each model are discussed. Recursive formulas for the conditional densities and the likelihood ratios are derived for many of the models. The additive measurement noise case is also considered in detail. An error criterion for direction estimation is presented with respect to which the optimal estimates can be easily computed from the probability distribution. A deficiency of the models and techniques developed in this paper is that random driving terms are disallowed in the signal processes.