Ruibin Zhang

Ruibin Zhang, Donglai Xue, Yuhan Wang et al.

Millimeter wave (mmWave) radars have attracted significant attention from both academia and industry due to their capability to operate in extreme weather conditions. However, they face challenges in terms of sparsity and noise interference, which hinder their application in the field of micro aerial vehicle (MAV) autonomous navigation. To this end, this paper proposes a novel approach to dense and accurate mmWave radar point cloud construction via cross-modal learning. Specifically, we introduce diffusion models, which possess state-of-the-art performance in generative modeling, to predict LiDAR-like point clouds from paired raw radar data. We also incorporate the most recent diffusion model inference accelerating techniques to ensure that the proposed method can be implemented on MAVs with limited computing resources.We validate the proposed method through extensive benchmark comparisons and real-world experiments, demonstrating its superior performance and generalization ability. Code and pretrained models will be available at https://github.com/ZJU-FAST-Lab/Radar-Diffusion.

Ruinan Ma, Canjie Zhu, Mingfeng Lu et al.

Electronic countermeasures involving radar signals are an important aspect of modern warfare. Traditional electronic countermeasures techniques typically add large-scale interference signals to ensure interference effects, which can lead to attacks being too obvious. In recent years, AI-based attack methods have emerged that can effectively solve this problem, but the attack scenarios are currently limited to time domain radar signal classification. In this paper, we focus on the time-frequency images classification scenario of radar signals. We first propose an attack pipeline under the time-frequency images scenario and DITIMI-FGSM attack algorithm with high transferability. Then, we propose STFT-based time domain signal attack(STDS) algorithm to solve the problem of non-invertibility in time-frequency analysis, thus obtaining the time-domain representation of the interference signal. A large number of experiments show that our attack pipeline is feasible and the proposed attack method has a high success rate.