计算机技术与发展2024，Vol.34Issue(5)：141-148,8.DOI:10.20165/j.cnki.ISSN1673-629X.2024.0052
基于贝叶斯估计和群体智能的无人机轨迹优化
UAV Trajectory Optimization Based on Bayesian Estimation and Swarm Intelligence
摘要
Abstract
To improve the localization accuracy and formation adjustment efficiency of UAVs,a positioning model based on Bayesian esti-mation and a formation adjustment method based on a swarm intelligence algorithm is proposed.Firstly,considering the influence of measurement noise in the actual situation,a new localization model is obtained by introducing maximum a posteriori estimation into the fixed string fixed angle model.Then,for the problem that the particle swarm algorithm tends to fall into local optimization,an improved queue adjustment algorithm is proposed in combination with a simulated annealing algorithm.The simulation results show that the error rate of the proposed localization model for circular(conical)formation is72.8%(49.2%)lower than that of the initial model.The error rate of the improved formation adjustment algorithm for circular(conical)formation is 37.1%(27.0%)and 24.7%(19.9%)lower than that of the original algorithm and the combined method genetic algorithm and Gauss pseudo spectral method respectively,while the number of convergence iterations decreases by 12.5%(20%)and 12.5%(4.8%)respectively.The experimental results verify the proposed optimization scheme's high accuracy and computational efficiency.关键词
无人机轨迹优化/无源定位/贝叶斯优化/粒子群算法/模拟退火算法Key words
unmanned aerial vehicles trajectory optimization/passive localization/Bayesian optimization/particle swarm algorithm/simulated annealing algorithm分类
信息技术与安全科学引用本文复制引用
丁汝妍,李欢,莫欣岳,吴灿,李昕雨..基于贝叶斯估计和群体智能的无人机轨迹优化[J].计算机技术与发展,2024,34(5):141-148,8.基金项目
教育部产学合作协同育人项目(220902070162538) （220902070162538）
中国高等教育学会高等教育科学研究规划课题(22LH0409) （22LH0409）
海南省自然科学基金(623RC455,623RC457) （623RC455,623RC457）
海南大学科研启动基金项目(KYQD(ZR)-22096,KYQD(ZR)-22097) （KYQD(ZR）
海南大学教育教学改革研究项目(hdjy2364) （hdjy2364）
