Integrated Sensing and Communication enabled Multiple Base Stations Cooperative UAV Detection

TL;DR

This paper presents a cooperative sensing method using multiple base stations and MUSIC-based fusion to improve UAV localization and velocity estimation accuracy in integrated sensing and communication systems.

Contribution

It introduces a novel symbol-level fusion approach combining preprocessing and lattice search for high-accuracy UAV sensing with multiple base stations.

Findings

Outperforms benchmark methods in localization accuracy

Enhances velocity estimation precision

Demonstrates effectiveness through extensive simulations

Abstract

Integrated sensing and communication (ISAC) exhibits notable potential for sensing the unmanned aerial vehicles (UAVs), facilitating real-time monitoring of UAVs for security insurance. Due to the low sensing accuracy of single base stations (BSs), a cooperative UAV sensing method by multi-BS is proposed in this paper to achieve high-accuracy sensing. Specifically, a multiple signal classification (MUSIC)-based symbol-level fusion method is proposed for UAV localization and velocity estimation, consisting of a single-BS preprocessing step and a lattice points searching step. The preprocessing procedure enhances the single-BS accuracy by superposing multiple spectral functions, thereby establishing a reference value for subsequent lattice points searching. Furthermore, the lattice point with minimal error compared to the preprocessing results is determined as the fusion result. Extensive simulation results reveal that the proposed symbol-level fusion method outperforms the benchmarking methods in localization and velocity estimation.