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

Anirban Banik; Lalitha Giridhar; Aaditya Prakash Kattekola; Anurag Pallaprolu; Yasamin Mostofi; Ashutosh Sabharwal; Upamanyu Madhow

arXiv · 2505.00857

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

Anirban Banik·Lalitha Giridhar·Aaditya Prakash Kattekola·Anurag Pallaprolu·Yasamin Mostofi·Ashutosh Sabharwal·Upamanyu Madhow

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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.

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BibTeX RIS

Anirban Banik, Lalitha Giridhar, Aaditya Prakash Kattekola, Anurag Pallaprolu, Yasamin Mostofi, Ashutosh Sabharwal, Upamanyu Madhow. 2025-05-01. mmSnap: Bayesian One-Shot Fusion in a Self-Calibrated mmWave Radar Network. https://arxiv.org/abs/2505.00857

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