  We consider the problem of active and sequential beam tracking at mmWave
frequencies and above. We focus on the dynamic scenario of a UAV to UAV
communications where we formulate the problem to be equivalent to tracking an
optimal beamforming vector along the line-of-sight path. In this setting, the
resulting beam ideally points in the direction of the angle of arrival with
sufficiently high resolution. Existing solutions account for predictable
movements or small random movements using filtering strategies or by accounting
for predictable mobility but must resort to re-estimation protocols when
tracking fails due to unpredictable movements. We propose an algorithm for
active learning of the AoA through evolving a Bayesian posterior probability
belief which is utilized for a sequential selection of beamforming vectors. We
propose an adaptive pilot allocation strategy based on a trade-off of mutual
information versus spectral efficiency. Numerically, we analyze the performance
of our proposed algorithm and demonstrate significant improvements over
existing strategies.
