The paper considers the problem of human-scale RF sensing utilizing a network of resource-constrained MIMO radars with low range-azimuth resolution. The radars operate in the mmWave band and obtain time-varying 3D point cloud (PC) information that is sensitive to body movements. They also observe the same scene from different views and cooperate while sensing the environment using a sidelink communication channel. Conventional cooperation setups allow the radars to mutually exchange raw PC information to improve ego sensing. The paper proposes a federation mechanism where the radars exchange the parameters of a Bayesian posterior measure of the observed PCs, rather than raw data. The radars act as distributed parameter servers to reconstruct a global posterior (i.e., federated posterior) using Bayesian tools. The paper quantifies and compares the benefits of radar federation with respect to cooperation mechanisms. Both approaches are validated by experiments with a real-time demonstration platform. Federation makes minimal use of the sidelink communication channel (20 {\div} 25 times lower bandwidth use) and is less sensitive to unresolved targets. On the other hand, cooperation reduces the mean absolute target estimation error of about 20%.

翻译：本文研究了利用资源受限、低距离-方位角分辨率的MIMO雷达网络进行人体尺度射频感知的问题。这些雷达工作在毫米波频段，获取对肢体运动敏感的时变三维点云信息。它们从不同视角观测同一场景，并在感知环境时通过侧行链路通信信道进行合作。传统合作机制允许雷达相互交换原始点云信息以提升自身感知性能。本文提出一种联邦机制，雷达间交换观测点云贝叶斯后验测度的参数而非原始数据。各雷达作为分布式参数服务器，利用贝叶斯工具重建全局后验（即联邦后验）。本文量化并比较了雷达联邦机制相对于合作机制的优势。两种方法均通过实时演示平台实验验证。联邦机制对侧行链路通信信道的使用需求极低（带宽占用降低20\div25倍），且对未解析目标不敏感。而合作机制可将平均目标估计绝对误差降低约20%。