Cooperative ISAC-empowered Low-Altitude Economy

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 develop a false removing minimum spanning tree (MST)-based data association method to accurately match the BSs' parameter estimations to the same UAV. Then, a Pareto optimality method and a residual weighting scheme are developed to facilitate the position and velocity estimation, respectively. We further extend our approach to the dual-polarized system. Simulation results validate the effectiveness of the proposed schemes in comparison to the conventional techniques.