  Radar must adapt to changing environments, and we propose changepoint
detection as a method to do so. In the world of increasingly congested radio
frequencies, radars must adapt to avoid interference. Many radar systems employ
the prediction action cycle to proactively determine transmission mode while
spectrum sharing. This method constructs and implements a model of the
environment to predict unused frequencies, and then transmits in this predicted
availability. For these selection strategies, performance is directly reliant
on the quality of the underlying environmental models. In order to keep up with
a changing environment, these models can employ changepoint detection.
Changepoint detection is the identification of sudden changes, or changepoints,
in the distribution from which data is drawn. This information allows the
models to discard "garbage" data from a previous distribution, which has no
relation to the current state of the environment. In this work, bayesian online
changepoint detection (BOCD) is applied to the sense and predict algorithm to
increase the accuracy of its models and improve its performance. In the context
of spectrum sharing, these changepoints represent interferers leaving and
entering the spectral environment. The addition of changepoint detection allows
for dynamic and robust spectrum sharing even as interference patterns change
dramatically. BOCD is especially advantageous because it enables online
changepoint detection, allowing models to be updated continuously as data are
collected. This strategy can also be applied to many other predictive
algorithms that create models in a changing environment.
