Enhanced Automotive Radar Collaborative Sensing By Exploiting Constructive Interference

TL;DR

This paper proposes a novel collaborative automotive radar sensing method that exploits constructive interference to improve target detection, reducing data sharing needs and bandwidth requirements.

Contribution

It introduces a new collaborative sensing scheme that aligns interference signals to enhance detection without extensive raw data exchange.

Findings

Significantly improves target detection accuracy.

Reduces data bandwidth needed for radar collaboration.

Demonstrates effectiveness through numerical simulations.

Abstract

Automotive radar emerges as a crucial sensor for autonomous vehicle perception. As more cars are equipped radars, radar interference is an unavoidable challenge. Unlike conventional approaches such as interference mitigation and interference-avoiding technologies, this paper introduces an innovative collaborative sensing scheme with multiple automotive radars that exploits constructive interference. Through collaborative sensing, our method optimally aligns cross-path interference signals from other radars with another radar's self-echo signals, thereby significantly augmenting its target detection capabilities. This approach alleviates the need for extensive raw data sharing between collaborating radars. Instead, only an optimized weighting matrix needs to be exchanged between the radars. This approach considerably decreases the data bandwidth requirements for the wireless channel, making it a more feasible and practical solution for automotive radar collaboration. Numerical results demonstrate the effectiveness of the constructive interference approach for enhanced object detection capability.