Cooperation and Federation in Distributed Radar Point Cloud Processing
<|reference_start|>Cooperation and Federation in Distributed Radar Point Cloud Processing: 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%.<|reference_end|>
@article{savazzi2024cooperation,
    title={Cooperation and Federation in Distributed Radar Point Cloud Processing},
    author={S. Savazzi, V. Rampa, S. Kianoush, A. Minora, L. Costa},
    journal={2023 IEEE 34th Annual International Symposium on Personal, Indoor
  and Mobile Radio Communications (PIMRC)},
    year={2024},
    doi={10.1109/PIMRC56721.2023.10294026},
    archivePrefix={arXiv},
    eprint={2405.01995},
    primaryClass={cs.LG cs.CV cs.IT math.IT}
}