Physics-Inspired Target Shape Detection and Reconstruction in mmWave Communication Systems

TL;DR

This paper introduces a physics-inspired approach for detecting and reconstructing the shape of remote targets in mmWave communication systems by combining scattering models, signal processing techniques, and geometric algorithms.

Contribution

It develops a novel reconstruction algorithm integrating Lambertian scattering, periodogram, subspace methods, Hough Transform, and PCA for shape detection in mmWave ISAC systems.

Findings

High sensing accuracy demonstrated in simulations

Effective reconstruction of convex polygon targets

Fusion of scattering and reflection points improves results

Abstract

The integration of sensing and communication (ISAC) is an essential function of future wireless systems. Due to its large available bandwidth, millimeter-wave (mmWave) ISAC systems are able to achieve high sensing accuracy. In this paper, we consider the multiple base-station (BS) collaborative sensing problem in a multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) mmWave communication system. Our aim is to sense a remote target shape with the collected signals which consist of both the reflection and scattering signals. We first characterize the mmWave's scattering and reflection effects based on the Lambertian scattering model. Then we apply the periodogram technique to obtain rough scattering point detection, and further incorporate the subspace method to achieve more precise scattering and reflection point detection. Based on these, a reconstruction algorithm based on Hough Transform and principal component analysis (PCA) is designed for a single convex polygon target scenario. To improve the accuracy and completeness of the reconstruction results, we propose a method to further fuse the scattering and reflection points. Extensive simulation results validate the effectiveness of the proposed algorithms.