# A Deep Learning Approach for Automotive Radar Interference Mitigation

## TL;DR

This paper introduces a deep learning method for automotive radar interference mitigation that outperforms traditional signal processing techniques in accuracy and speed, enhancing autonomous driving systems.

## Contribution

The paper presents a novel deep learning approach specifically designed to mitigate radar interference, overcoming limitations of conventional methods.

## Key findings

- Achieves higher interference cancellation performance than existing methods
- Demonstrates robustness across various interference scenarios
- Offers low processing time suitable for real-time applications

## Abstract

In automotive systems, a radar is a key component of autonomous driving. Using transmit and reflected radar signal by a target, we can capture the target range and velocity. However, when interference signals exist, noise floor increases and it severely affects the detectability of target objects. For these reasons, previous studies have been proposed to cancel interference or reconstruct original signals. However, the conventional signal processing methods for canceling the interference or reconstructing the transmit signals are difficult tasks, and also have many restrictions. In this work, we propose a novel approach to mitigate interference using deep learning. The proposed method provides high performance in various interference conditions and has low processing time. Moreover, we show that our proposed method achieves better performance compared to existing signal processing methods.