Performance Evaluation and Analysis of Thresholding-based Interference Mitigation for Automotive Radar Systems
In automotive radar, time-domain thresholding (TD-TH) and time-frequency domain thresholding (TFD-TH) are crucial techniques underpinning numerous interference mitigation methods. Despite their importance, comprehensive evaluations of these methods in dense traffic scenarios with different types of interference are limited. In this study, we segment automotive radar interference into three distinct categories. Utilizing the in-house traffic scenario and automotive radar simulator, we evaluate interference mitigation methods across multiple metrics: probability of detection, signal-to-interference-plus-noise ratio, and phase error involving hundreds of targets and dozens of interfering radars. The numerical results highlight that TFD-TH is more effective than TD-TH, particularly as the density and signal correlation of interfering radars escalate.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Reinforcement Learning for Mitigating Intermittent Interference in Terahertz Communication Networks
Emerging wireless services with extremely high data rate requirements, such as real-time extended reality applications, mandate novel solutions to further increase the capacity of future wireless networks. In this regard…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Compressed Sensing Based RFI Mitigation and Restoration for Pulsar Signals
In pulsar signal processing, two primary difficulties are (1) radio-frequency interference (RFI) mitigation and (2) information loss due to preprocessing and mitigation itself. Linear mitigation methods have a difficult…
compressed sensingMutual Interference Mitigation for MIMO-FMCW Automotive Radar
This paper considers mutual interference mitigation among automotive radars using frequency-modulated continuous wave (FMCW) signal and multiple-input multiple-output (MIMO) virtual arrays. For the first time, we derive …
object-detectionObject DetectionDeep Interference Mitigation and Denoising of Real-World FMCW Radar Signals
Radar sensors are crucial for environment perception of driver assistance systems as well as autonomous cars. Key performance factors are a fine range resolution and the possibility to directly measure velocity. With a r…
DenoisingTransfer LearningPrior-Guided Deep Interference Mitigation for FMCW Radars
A prior-guided deep learning (DL) based interference mitigation approach is proposed for frequency modulated continuous wave (FMCW) radars. In this paper, the interference mitigation problem is tackled as a regression pr…