A Fast Multi-UAV Assistance Positioning Architecture for Large-Scale Farming Operations

Autonomous intelligent agricultural vehicles significantly reduce labor requirements in large-scale farming operations. While real-time precise positioning of agricultural vehicles is prerequisite for autonomous operation, Global Positioning System (GPS)-based positioning systems demonstrate insufficient accuracy in large-scale farming scenarios. Moreover, the limited infrastructure deployment and compromised network quality in extensive agricultural fields render conventional assistance positioning frameworks inadequate. Unmanned Aerial Vehicles (UAVs), as integral components of vehicular networks, facilitate positioning and navigation within Space-Air-Ground Integrated Networks (SAGIN). Based on this, this paper proposes a multi-UAV rapid positioning assistance architecture for large-scale farming operations. Under this architecture, Local Area Networks (LANs) are established between UAVs and agricultural vehicles, with each vehicle equipped with a Uniform Linear Array (ULA). The system requires only signal transmission from UAVs, while the agricultural vehicles’ local processing systems perform Direction of Arrival (DOA) estimation, followed by joint estimation to determine vehicle positioning coordinates. However, limited signal snapshots in practical DOA estimation environments compromise estimation accuracy. While Compressed Sensing (CS) algorithms demonstrate significant potential for DOA estimation applications, conventional CS methodologies employing Uniform Overcomplete Dictionaries (UOD) in sparse representation phase incur substantial computational complexity. Additionally, the Orthogonal Matching Pursuit (OMP) algorithm and its derivatives exhibit susceptibility to local optima convergence during signal reconstruction. Consequently, this paper proposes the NODGA-CS model, integrating Non-uniform Overcomplete Dictionary (NOD) with Genetic Algorithm (GA) optimization for enhanced CS-DOA estimation. Experimental results demonstrate that the proposed assistance architecture achieves superior positioning accuracy when benchmarked against state-of-the-art positioning frameworks.