The rapid evolution of millimeter-wave radar sensing technology has given rise to a proliferation of open-source radar datasets, creating an urgent need for innovative digital copyright protection techniques. However, conventional image and audio watermarking techniques are inadequate for radar copyright protection due to radar signals' sparsity, vulnerability and complexity. In this paper, we present RFinger, an ownership verification framework for static indoor millimeter-wave radar datasets. Our approach encodes environmental information extracted from radar signals into digital watermarks, strategically embedding these within carefully selected data frames to establish robust verification credentials. We develop statistical hypothesis testing metrics to detect unauthorized access to RFinger-protected data in black-box setting. Our strategic watermark design ensures that the unauthorized models exhibit distinctly anomalous performance on verification data compared to legitimate models. Through experiments on two large millimeter-wave radar datasets, we have validated that our designed strategy provides high watermark retrieval accuracy without compromising downstream tasks.

Human tracking plays a crucial role in various wireless sensing applications. However, recent advancements have primarily focused on constrained experimental scenarios with less interference, often involving a few individuals performing actions in an empty space without obstacles. In empirical unconstrained scenarios, such as daily office scenes, severe interference and attenuation caused by chaotic environments is inevitable which results in dramatic performance degradation. In this paper, we introduce TBDNet, which incorporates tracking-before-detect (TBD) from conventional signal processing into learning-based models, achieving impressive tracking performance in unconstrained scenarios. TBDNet follows first-track-then-detect pipeline. It maps input heatmap sequence into high-level frame-wise features to adapt the time-varying intensity distribution and motion pattern of targets. After that, the temporal information is accumulated in feature space to obtain trace proposals. We then predict the accurate positions and probability of traces at each timestamp. To assess the efficiency of TBDNet, we collect and release the first RF-UNIT (RF-based Unconstrained Indoor Tracking) dataset, which comprises 4,030,880 radar heatmaps and the corresponding tracking annotations under 6 different scenarios. To our knowledge, RF-UNIT is the first dataset for RF-based human tracking in unconstrained scenes. We anticipate that TBDNet and the RF-UNIT dataset will significantly contribute to the advancement of RF-based sensing technologies.