Multi UAV-enabled Distributed Sensing: Cooperation Orchestration and Detection Protocol

TL;DR

This paper introduces a UAV-based distributed sensing framework using OFDM waveforms and cooperative algorithms to improve ground target detection accuracy and resolution, outperforming single UAV benchmarks and reducing overhead.

Contribution

It presents a novel distributed sensing framework with joint RCS estimation, digital beamforming, and fusion center coordination, enhancing detection performance over traditional methods.

Findings

Improved accuracy and resolution compared to single UAV systems

Reduced overhead relative to compressive sensing approaches

Effective joint estimation of RCS in a spatial grid

Abstract

This paper proposes an unmanned aerial vehicle (UAV)-based distributed sensing framework that uses orthogonal frequency-division multiplexing (OFDM) waveforms to detect the position of a ground target, and UAVs operate in half-duplex mode. A spatial grid approach is proposed, where an specific area in the ground is divided into cells of equal size, then the radar cross-section (RCS) of each cell is jointly estimated by a network of dual-function UAVs. For this purpose, three estimation algorithms are proposed employing the maximum likelihood criterion, and digital beamforming is used for the local signal acquisition at the receive UAVs. It is also considered that the coordination, fusion of sensing data, and central estimation is performed at a certain UAV acting as a fusion center (FC). Monte Carlo simulations are performed to obtain the absolute estimation error of the proposed framework. The results show an improved accuracy and resolution by the proposed framework, if compared to a single monostatic UAV benchmark, due to the distributed approach among the UAVs. It is also evidenced that a reduced overhead is obtained when compared to a general compressive sensing (CS) approach.