3GPP-Compliant Radar Cross Section Characterization of Indoor Factory Targets

TL;DR

This paper provides a systematic 3GPP-compliant characterization of radar cross section (RCS) for indoor factory targets, validating models and revealing size-dependent reflectivity patterns for UAVs, robotic arms, and AGVs in the 25-28 GHz range.

Contribution

It offers the first empirical validation of 3GPP RCS models for indoor factory targets, including UAVs and robotic equipment, in the 25-28 GHz band.

Findings

3GPP RCS model validated with <1 dB deviation for small UAVs.

Mid-sized UAVs show higher reflectivity due to material and batteries.

Robotic arms exhibit dynamic RCS behavior due to articulation.

Abstract

The following paper presents a systematic 3rd Generation Partnership Project (3GPP)-compliant characterization of radar cross section (RCS) for indoor factory (InF) objects, including small and mid-sized unmanned aerial vehicles (UAVs), robotic arms, and automated guided vehicles (AGVs). Through measurements in the 25-28 GHz range, we validate the 3GPP standardized log-normal distribution model for RCS for above-mentioned target objects. The 3GPP-complaint RCS parameters obtained for the small-sized UAV are in close agreement (<1 dB deviation) with 3GPP agreed values. The mid-sized UAVs exhibit higher reflectivity compared to the small-sized UAV due to enhanced specular components attributed to material and lithium-ion battery packs. The robotic arm exhibits dynamic RCS behavior due to mechanical articulation, whereas UAVs show clear size-dependent reflectivity patterns in AGVs. Our findings provide empirical validation for RCS characterization for integrated sensing and communication channel modeling in InF environments.