Deep Radar Waveform Design for Efficient Automotive Radar Sensing

TL;DR

This paper introduces a hybrid deep learning approach for adaptive unimodular radar waveform design, enhancing automotive radar performance in dynamic environments with low-cost implementation.

Contribution

It presents a novel hybrid model-driven and data-driven architecture that adapts to changing environments for efficient radar waveform design in autonomous vehicles.

Findings

Improves clutter and interference rejection.

Enables real-time adaptive waveform design.

Demonstrates effectiveness in time-varying environments.

Abstract

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.