Statistical and Deterministic RCS Characterization for ISAC Channel Modeling

TL;DR

This paper analyzes the radar cross section (RCS) of various indoor targets at 25-28 GHz, developing statistical and deterministic models for target identification and sensing in future wireless systems.

Contribution

It introduces novel statistical and deterministic RCS models that incorporate bistatic angle, T-R distance, and target characteristics, validated with extensive measurements.

Findings

Lognormal and gamma distributions effectively model RCS data.

Proposed RCS models fit measured data accurately in bistatic configurations.

Framework for evaluating deterministic RCS of laminated wood targets.

Abstract

In this study, we perform a statistical analysis of the radar cross section (RCS) for various test targets in an indoor factory at \(25\)-\(28\) GHz, with the goal of formulating parameters that may be used for target identification and other sensing applications for future wireless systems. The analysis is conducted based on measurements in monostatic and bistatic configurations for bistatic angles of \(20^\circ\), \(40^\circ\), and \(60^\circ\), which are functions of transmitter-receiver (T-R) and target positions, via accurate \(3\)dB beamwidth of \(10^\circ\) in both azimuth and elevation planes. The test targets include unmanned aerial vehicles, an autonomous mobile robot, and a robotic arm. We utilize parametric statistical distributions to fit the measured RCS data. The analysis reveals that the \textit{lognormal and gamma distributions} are effective in modeling the RCS of the test targets over different reflecting points of the target itself, i.e. when target is in motion. Additionally, we provide a framework for evaluating the deterministic bistatic RCS of a rectangular sheet of laminated wood, due to its widespread use in indoor hotspot environments. Novel deterministic and statistical RCS models are evaluated, incorporating dependencies on the bistatic angle, T-R distance (\(2\)m -\(10\)m) and the target. The results demonstrate that some proposed RCS models accurately fit the measured data, highlighting their applicability in bistatic configurations.