RIMformer: An End-to-End Transformer for FMCW Radar Interference Mitigation

TL;DR

This paper introduces RIMformer, a novel end-to-end Transformer-based method for mitigating interference in FMCW radar signals, improving detection accuracy and system reliability.

Contribution

The paper proposes a new Transformer-based architecture with dual multi-head self-attention and an improved convolutional block for FMCW radar interference mitigation.

Findings

Effective interference mitigation demonstrated in simulations

Restores target signals accurately in measurement experiments

Improves radar detection reliability

Abstract

Frequency-modulated continuous-wave (FMCW) radar plays a pivotal role in the field of remote sensing. The increasing degree of FMCW radar deployment has increased the mutual interference, which weakens the detection capabilities of radars and threatens reliability and safety of systems. In this paper, a novel FMCW radar interference mitigation (RIM) method, termed as RIMformer, is proposed by using an end-to-end Transformer-based structure. In the RIMformer, a dual multi-head self-attention mechanism is proposed to capture the correlations among the distinct distance elements of intermediate frequency (IF) signals. Additionally, an improved convolutional block is integrated to harness the power of convolution for extracting local features. The architecture is designed to process time-domain IF signals in an end-to-end manner, thereby avoiding the need for additional manual data processing steps. The improved decoder structure ensures the parallelization of the network to increase its computational efficiency. Simulation and measurement experiments are carried out to validate the accuracy and effectiveness of the proposed method. The results show that the proposed RIMformer can effectively mitigate interference and restore the target signals.