Cooperation and Federation in Distributed Radar Point Cloud Processing

TL;DR

This paper introduces a federated radar sensing approach that exchanges Bayesian posterior parameters instead of raw data, reducing communication bandwidth and improving robustness in distributed 3D point cloud sensing.

Contribution

It proposes a novel federation mechanism for distributed radar networks that enhances sensing efficiency and robustness by sharing Bayesian posterior parameters rather than raw data.

Findings

Federation reduces sidelink bandwidth use by 20-25 times.

Federation is less sensitive to unresolved targets.

Cooperation improves target estimation accuracy by about 20%.

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 {\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%.