Abstract
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 ÷ 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%.
Abstract (translated)
本文考虑了利用具有低距离角分辨率、资源受限的MIMO雷达网络进行人规模射频感知的問題。这些雷达在毫米波频段运行，并获取对体运动敏感的时间变化3D点云（PC）信息。它们还从不同的视角观察相同的场景，并通过侧链通信通道同时感測环境。传统的合作方案使雷达能够相互交换原始PC信息以提高自感。本文提出了一种联邦机制，其中雷达交换的是观察到的PC的后验分布参数，而不是原始数据。雷达作为分布式参数服务器，使用贝叶斯工具重建全局后验（即联邦后验）。本文对雷达联邦与合作机制的优劣进行了定量和比较。两种方法都在实时演示平台上通过实验进行了验证。联邦对侧链通信通道（20/25倍带宽利用率）的使用最少，对未解决的目标不敏感。另一方面，合作减少了大约20%的目标绝对估计误差。
URL
https://arxiv.org/abs/2405.01995
