  The concept of cognitive radar (CR) enables radar systems to achieve
intelligent adaption to a changeable environment with feedback facility from
receiver to transmitter. However, the implementation of CR in a fast-changing
environment usually requires a well-known environmental model. In our work, we
stress the learning ability of CR in an unknown environment using a combination
of CR and reinforcement learning (RL), called RL-CR. Less or no model of the
environment is required. We also apply the general RL-CR to a specific problem
of automotive radar spectrum allocation to mitigate mutual interference. Using
RL-CR, each vehicle can autonomously choose a frequency subband according to
its own observation of the environment. Since radar's single observation is
quite limited compared to the overall information of the environment, a long
short-term memory (LSTM) network is utilized so that radar can decide the next
transmitted subband by aggregating its observations over time. Compared with
centralized spectrum allocation approaches, our approach has the advantage of
reducing communication between vehicles and the control center. It also
outperforms some other distributive frequency subband selecting policies in
reducing interference under certain circumstances.
