$Radar^2$: Passive Spy Radar Detection and Localization using COTS mmWave Radar [PDF]
IEEE Transactions on Information Forensics and Security, 2022 Millimeter-wave (mmWave) radars have found applications in a wide range of domains, including human tracking, health monitoring, and autonomous driving, for their unobtrusive nature and high range accuracy. These capabilities, however, if used for malicious purposes, could also result in serious security and privacy issues.
Yanlong Qiu +4 more
arxiv +3 more sources
Impact analysis of DRFM-based active jamming to radar detection efficiency
The Journal of Engineering, 2019 The influence of DRFM active jamming technology on pulse compression coherent radar is studied here. By establishing radar signal detection model and introducing radar equation, the influence of signal produced by DRFM on the detection performance of ...
Wei Liu, Jin Meng, Liang Zhou
doaj +2 more sources
Study on Quantum Radar Detection Probability Based on Flying-Wing Stealth Aircraft. [PDF]
Sensors (Basel), 2022 The development of quantum radar technology presents a challenge to stealth targets, so it is necessary to study the quantum detection probability. In this study, an analytical expression of the quantum radar cross section (QRCS) for complex targets is ...
Lu S, Meng Z, Huang J, Yi M, Wang Z.
europepmc +2 more sources
Spatial Diversity in Radar Detection via Active Reconfigurable Intelligent Surfaces [PDF]
IEEE Signal Processing Letters, 2022 Active reconfigurable intelligent surfaces (RISs) are a novel and promising technology that allows controlling the radio propagation environment while compensating for the product path loss along the RIS-assisted path.
Mohamed Rihan +3 more
semanticscholar +1 more source
Contrastive Learning for Automotive mmWave Radar Detection Points Based Instance Segmentation [PDF]
2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), 2022 The automotive mmWave radar plays a key role in advanced driver assistance systems (ADAS) and autonomous driving. Deep learning-based instance segmentation enables real-time object identification from the radar detection points.
Weiyi Xiong +5 more
semanticscholar +1 more source
Foundations of MIMO Radar Detection Aided by Reconfigurable Intelligent Surfaces [PDF]
IEEE Transactions on Signal Processing, 2021 A reconfigurable intelligent surface (RIS) is a nearly-passive flat layer made of inexpensive elements that can add a tunable phase shift to the impinging electromagnetic wave and are controlled by a low-power electronic circuit. This paper considers the
S. Buzzi +3 more
semanticscholar +1 more source
Deep Instance Segmentation With Automotive Radar Detection Points [PDF]
IEEE Transactions on Intelligent Vehicles, 2021 Automotive radar provides reliable environmental perception in all-weather conditions with affordable cost, but it hardly supplies semantic and geometry information due to the sparsity of radar detection points.
Jianan Liu +6 more
semanticscholar +1 more source
RADAR: Robust AI-Text Detection via Adversarial Learning [PDF]
Neural Information Processing Systems, 2023 Recent advances in large language models (LLMs) and the intensifying popularity of ChatGPT-like applications have blurred the boundary of high-quality text generation between humans and machines.
Xiaomeng Hu, Pin-Yu Chen, Tsung-Yi Ho
semanticscholar +1 more source
Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Review [PDF]
IEEE Transactions on Intelligent Vehicles, 2023 Driven by deep learning techniques, perception technology in autonomous driving has developed rapidly in recent years, enabling vehicles to accurately detect and interpret surrounding environment for safe and efficient navigation. To achieve accurate and
Shanliang Yao +10 more
semanticscholar +1 more source
SMURF: Spatial Multi-Representation Fusion for 3D Object Detection With 4D Imaging Radar [PDF]
IEEE Transactions on Intelligent Vehicles, 2023 The 4D millimeter-Wave (mmWave) radar is a promising technology for vehicle sensing due to its cost-effectiveness and operability in adverse weather conditions.
Jianan Liu +5 more
semanticscholar +1 more source