一种无人机飞行自组织网络的多目标优化方案

Multi-Objective Optimization Scheme for UAV Flying Ad Hoc Network

飞行自组织网络(FANET)是一种稳健而灵活的无线通信方式,涉及信号覆盖范围、吞吐量和电池消耗等关键因素。然而,优化这些参数仍然面临着实际挑战。本文使用递归神经网络(RNN)对影响因素进行建模,并引入自适应预测控制(APC)方法构建控制系统,深入研究机载Wi-Fi网络和FANET实验系统的综合性能。通过智能的Ad Hoc网络和AP控制方法,研究团队成功优化了无人机的通信系统容量和覆盖问题,为飞机控制带来了显著优势。

Flying Ad Hoc Network(FANET)is a robust and flexible wireless communication method that involves key factors such as signal coverage,throughput,and battery consumption.However,there are still practical challenges to optimizing these parameters.In this paper,recurrent neural network(RNN)is used to model the influencing factors,and the adaptive predictive control(APC)method is introduced to construct the control system,and the comprehensive performance of the airborne Wi-Fi network and the FANET experimental system is deeply studied.Through the intelligent Ad Hoc network and AP control method,the research team successfully optimizes the communication system capacity and coverage of the UAV,which has significant advantages to the aircraft control.