mmSnap: Bayesian One-Shot Fusion in a Self-Calibrated mmWave Radar Network

TL;DR

mmSnap introduces a Bayesian one-shot fusion method for mmWave radar networks, enabling accurate real-time tracking through self-calibration and collaborative data fusion, validated with outdoor experiments.

Contribution

The paper presents a novel Bayesian fusion framework and a self-calibration algorithm for mmWave radar networks, enhancing real-time multi-node sensing capabilities.

Findings

Effective self-calibration of radar nodes in closed form

Accurate one-shot position and velocity estimation

Validation with outdoor human target tracking

Abstract

We present mmSnap, a collaborative RF sensing framework using multiple radar nodes, and demonstrate its feasibility and efficacy using commercially available mmWave MIMO radars. Collaborative fusion requires network calibration, or estimates of the relative poses (positions and orientations) of the sensors. We experimentally validate a self-calibration algorithm developed in our prior work, which estimates relative poses in closed form by least squares matching of target tracks within the common field of view (FoV). We then develop and demonstrate a Bayesian framework for one-shot fusion of measurements from multiple calibrated nodes, which yields instantaneous estimates of position and velocity vectors that match smoothed estimates from multi-frame tracking. Our experiments, conducted outdoors with two radar nodes tracking a moving human target, validate the core assumptions required to develop a broader set of capabilities for networked sensing with opportunistically deployed nodes.