电子科技2026，Vol.39Issue(8)：25-31,7.DOI:10.16180/j.cnki.issn1007-7820.2026.08.004
基于蛇鹭优化算法的多站协同构型优化方法
Multi Station Collaborative Configuration Optimization Method Based on Snake Heron Optimization Algorithm
摘要
Abstract
The optimization of the types and quantities of multi-station deployment is a hot issue in the field of passive positioning.The quality of station deployment directly affects the accuracy of target positioning and track-ing.Limited by the convergence ability of the optimization algorithm,the traditional algorithm has a slow conver-gence speed and can only find suboptimal solutions as alternative configurations,which affects the positioning per-formance of passive localization.In this study,GDOP(Geometric Dilution of Precision)is used to achieve efficient site deployment by improving the snake-heron optimization algorithm.To address the issue of slow convergence speed of the optimization algorithm,an adaptive distance limit between two observation stations is proposed to pre-vent the snake-heron optimization algorithm from being trapped in local optimal solutions for a long time.In view of the problem of poor stability of the optimization algorithm,it is proposed to adopt binary coding to simulate the itera-tive way of species renewal in nature to enhance the diversity of population renewal.In view of the insufficient con-vergence ability of the optimization algorithm,a hybrid operator model is proposed for species update and iteration.The snake-heron optimization algorithm is improved to adapt to the multi-station configuration optimization prob-lem.The experimental results show that in terms of optimization ability and optimization speed,the performance of the proposed algorithm is superior to that of the traditional algorithm.关键词
时差定位/构型优化/定位精度/无源定位/蛇鹭优化/多站协同/站址布局/无人机Key words
TDOA/configuration optimization/GDOP/passive location/snake heron optimization/multi-site collaboration/site layout/UVA分类
信息技术与安全科学引用本文复制引用
邱子成,陈思远,张鑫焱,包敏,姚书剑,范刚,未履伦..基于蛇鹭优化算法的多站协同构型优化方法[J].电子科技,2026,39(8):25-31,7.基金项目
国家重点研发计划(2021YFC3090402)National Key R&D Program(2021YFC3090402) （2021YFC3090402）
