Cooperative ISAC-empowered Low-Altitude Economy
Jun Tang, Yiming Yu, Cunhua Pan, Hong Ren, Dongming Wang, Jiangzhou, Wang, Xiaohu You

TL;DR
This paper introduces a cooperative ISAC scheme for low-altitude UAV sensing, combining tensor decomposition, reduced-dimensional AoA estimation, and data fusion across multiple base stations to improve UAV parameter estimation accuracy.
Contribution
It presents a novel two-stage cooperative sensing scheme with tensor-based parameter estimation and data fusion methods, extending to dual-polarized systems.
Findings
Enhanced UAV parameter estimation accuracy
Effective data fusion across multiple base stations
Validated performance improvements through simulations
Abstract
This paper proposes a cooperative integrated sensing and communication (ISAC) scheme for the low-altitude sensing scenario, aiming at estimating the parameters of the unmanned aerial vehicles (UAVs) and enhancing the sensing performance via cooperation. The proposed scheme consists of two stages. In Stage I, we formulate the monostatic parameter estimation problem via using a tensor decomposition model. By leveraging the Vandermonde structure of the factor matrix, a spatial smoothing tensor decomposition scheme is introduced to estimate the UAVs' parameters. To further reduce the computational complexity, we design a reduced-dimensional (RD) angle of arrival (AoA) estimation algorithm based on generalized Rayleigh quotient (GRQ). In Stage II, the positions and true velocities of the UAVs are determined through the data fusion across multiple base stations (BSs). Specifically, we first…
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Taxonomy
TopicsDistributed Control Multi-Agent Systems
MethodsBalanced Selection
