Experimental Demonstration of Multi-Target Tracking in Integrated Sensing and Communication
Maximilian Bauhofer, Marcus Henninger, Meik Kottkamp, Lucas Giroto, Philip Grill, Alexander Felix, Thorsten Wild, Stephan ten Brink, Silvio Mandelli

TL;DR
This paper demonstrates multi-target tracking using a PHD filter in a real 5G ISAC setup within a factory, showing promising results despite real-world challenges.
Contribution
It is the first to apply multi-target tracking algorithms to real-world ISAC data, addressing practical challenges like clutter and hardware limitations.
Findings
Mean absolute ranging error <1.5m
Detection rates >91% in challenging scenarios
Successful end-to-end pipeline from measurement to evaluation
Abstract
For a wide range of envisioned integrated sensing and communication (ISAC) use cases, it is necessary to incorporate tracking techniques into cellular communication systems. While numerous multi-target tracking (MTT) algorithms exist, they have not yet been applied to real-world ISAC, with its challenges such as clutter and non-optimal hardware with design emphasis on communication instead of sensing. In this work, we showcase MTT based on the probability hypothesis density (PHD) filter in the range and radial speed domain. The measurements are taken with a 5G compliant ISAC proof-of-concept in a real factory environment, where the pedestrian-like targets are generated by a radar target emulator. We detail the complete pipeline, from measurement acquisition to evaluation, with a focus on the post-processing of the raw captured data and the tracking itself. Our end-to-end evaluation and…
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