  Autonomous robots operating in dynamic environments must maintain beliefs
over a hypothesis space that is rich enough to represent the activities of
interest at different scales. This is important both in order to accommodate
the availability of evidence at varying degrees of coarseness, such as when
interpreting and assimilating natural instructions, but also in order to make
subsequent reactive planning more efficient. We present an algorithm that
combines a topology-based trajectory clustering procedure that generates
hierarchically-structured spatial abstractions with a bank of particle filters
at each of these abstraction levels so as to produce probability estimates over
an agent's navigation activity that is kept consistent across the hierarchy. We
study the performance of the proposed method using a synthetic trajectory
dataset in 2D, as well as a dataset taken from AIS-based tracking of ships in
an extended harbour area. We show that, in comparison to a baseline which is a
particle filter that estimates activity without exploiting such structure, our
method achieves a better normalised error in predicting the trajectory as well
as better time to convergence to a true class when compared against ground
truth.
