Real-Time Multi-Target Detection and Tracking with mmWave 5G NR Waveforms on RFSoC

TL;DR

This paper presents a real-time, hardware-accelerated system for multi-target detection and tracking using 5G NR waveforms at 28 GHz on an RFSoC platform, combining advanced signal processing with flexible computing resources.

Contribution

The work demonstrates a fully hardware-accelerated, real-time multi-target detection and tracking system utilizing 5G NR waveforms on RFSoC, integrating sensing, processing, and beam control.

Findings

Achieved real-time multi-target detection and tracking at 28 GHz.

Implemented low-latency processing entirely in programmable logic.

Integrated heterogeneous computing resources for flexible system design.

Abstract

We demonstrate a real-time implementation of multi-target detection and tracking using 5G New Radio (NR) physical downlink shared channel (PDSCH) waveform with 400 MHz bandwidth at 28 GHz carrier frequency. The hardware platform is built on a radio frequency system-on-chip (RFSoC) 4x2 board connected with a pair of Sivers EVK02001 mmWave beamformers for transmission and reception. The entire sensing transceiver processing and fast beam control are realized purely in the programmable logic (PL) part of the RFSoC, enabling low-latency and fully hardware-accelerated operation. The continuously acquired sensing data constitute 3D range-angle (RA) tensors, which are processed on a host PC using adaptive background subtraction, cell-averaging constant false alarm rate (CA-CFAR) detection with density-based spatial clustering of applications with noise (DBSCAN) clustering, and extended Kalman filtering (EKF), to detect and track targets in the environment. Our software-defined radio (SDR) testbed integrates heterogeneous computing resources, including CPUs, GPUs, and FPGAs, thereby providing design flexibility for a wide range of tasks.