  Improved communication systems, shrinking battery sizes and the price drop of
tracking devices have led to an increasing availability of trajectory tracking
data. These data are often analyzed to understand animal behavior.
  In this work, we propose a new model for interpreting the animal movent as a
mixture of characteristic patterns, that we interpret as different behaviors.
The probability that the animal is behaving according to a specific pattern, at
each time instant, is non-parametrically estimated using the Logistic-Gaussian
process. Owing to a new formalization and the way we specify the
coregionalization matrix of the associated multivariate Gaussian process, our
model is invariant with respect to the choice of the reference element and of
the ordering of the probability vector components. We fit the model under a
Bayesian framework, and show that the Markov chain Monte Carlo algorithm we
propose is straightforward to implement.
  We perform a simulation study with the aim of showing the ability of the
estimation procedure to retrieve the model parameters. We also test the
performance of the information criterion we used to select the number of
behaviors. The model is then applied to a real dataset where a wolf has been
observed before and after procreation. The results are easy to interpret, and
clear differences emerge in the two phases.
