  We introduce geometric and topological methods to develop a new framework for
fusing multi-sensor time series. This framework consists of two steps: (1) a
joint delay embedding, which reconstructs a high-dimensional state space in
which our sensors correspond to observation functions, and (2) a simple
orthogonalization scheme, which accounts for tangencies between such
observation functions, and produces a more diversified geometry on the
embedding space. We conclude with some synthetic and real-world experiments
demonstrating that our framework outperforms traditional metric fusion methods.
