Deep Radar Waveform Design for Efficient Automotive Radar Sensing
ID:123 View Protection:ATTENDEE Updated Time:2020-08-05 10:17:28 Hits:359 Oral Presentation

Start Time:2020-06-08 14:20(Asia/Shanghai)


Session:S Special Session » SS10Automotive Radar Sensing

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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 \textit{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.
automotive radar; deep learning; deep unfolding; data-driven approaches; model-based signal processing; unimodular waveform design
Shahin Khobahi
University of Illinois at Chicago, USA

Submission Author
Shahin Khobahi University of Illinois at Chicago, USA
Arindam Bose University of Illinois at Chicago, USA
Mojtaba Soltanalian University of Illinois at Chicago, USA
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