  In radar systems, unimodular (or constant-modulus) waveform design plays an
important role in achieving better clutter/interference rejection, as well as a
more accurate estimation of the target parameters. The design of such sequences
has been studied widely in the last few decades, with most design algorithms
requiring sophisticated a priori knowledge of environmental parameters which
may be difficult to obtain in real-time scenarios. In this paper, we propose a
novel hybrid model-driven and data-driven architecture that adapts to the ever
changing environment and allows for adaptive unimodular waveform design. In
particular, the approach lays the groundwork for developing extremely low-cost
waveform design and processing frameworks for radar systems deployed in
autonomous vehicles. The proposed model-based deep architecture imitates a
well-known unimodular signal design algorithm in its structure, and can quickly
infer statistical information from the environment using the observed data. Our
numerical experiments portray the advantages of using the proposed method for
efficient radar waveform design in time-varying environments.
