3D Super-Resolution Imaging Method for Distributed Millimeter-wave Automotive Radar System

TL;DR

This paper introduces a 3D super-resolution imaging method for distributed millimeter-wave automotive radar systems, overcoming resolution limitations caused by small antenna apertures through a novel sparse array processing approach.

Contribution

It proposes a new 3D super-resolution imaging technique tailored for sparse MIMO radar arrays, combining range FFT, ADL-IAA, and CFAR processing.

Findings

Significantly improves imaging resolution with sparse arrays

Effective in single-measurement scenarios

Enhances automotive radar imaging capabilities

Abstract

Millimeter-wave (mmW) radar is widely applied to advanced autopilot assistance systems. However, its small antenna aperture causes a low imaging resolution. In this paper, a new distributed mmW radar system is designed to solve this problem. It forms a large sparse virtual planar array to enlarge the aperture, using multiple-input and multiple-output (MIMO) processing. However, in this system, traditional imaging methods cannot apply to the sparse array. Therefore, we also propose a 3D super-resolution imaging method specifically for this system in this paper. The proposed method consists of three steps: (1) using range FFT to get range imaging, (2) using 2D adaptive diagonal loading iterative adaptive approach (ADL-IAA) to acquire 2D super-resolution imaging, which can satisfy this sparsity under single-measurement, (3) using constant false alarm (CFAR) processing to gain final 3D super-resolution imaging. The simulation results show the proposed method can significantly improve imaging resolution under the sparse array and single-measurement.