基于粒子群算法优化极限学习机的无源目标定位算法  

PASSIVE TARGETS LOCALISATION ALGORITHM BASED ON OPTIMISING EXTREME LEARNING MACHINE WITH PSO

作　　者：傅彬[1] 

机构地区：[1]绍兴职业技术学院,浙江绍兴312000

出　　处：《计算机应用与软件》2015年第11期325-328,共4页Computer Applications and Software

基　　金：浙江省教育厅科研项目(Y201431515);浙江省高职高专院校特色专业建设项目(TZZ09078)

摘　　要：为了提高目标定位精度,提出一种基于粒子群算法优化极限学习机的无源目标定位算法。首先通过位置信息场采集目标的相关信息,然后利用极限学习机对位置信息场与目标位置之间的非映射关系进行拟合,同时采用粒子群算法对极限学习机参数进行优化,最后在Matlab 2009平台进行仿真对比实验。结果表明,相对于其他目标定位算法,该算法提高了目标定位的精度,更加适合于复杂环境下的目标定位。In order to improve the localisation accuracy, in this paper we put forward a passive target localisation algorithm which is based on optimising extreme learning machine with particle swarm optimisation （PSO）. Firsi, we collect correlated information of target through position information field, and then use extreme learning machine to fit the non-mapping relationship between position information field and target position, meanwhile we use PSO to optimise the parameters of extreme learning machine. Finally, we carry out simulation comparative experiment on MATLAB 2009 platform. Results show that relative to other target localisation algorithms, the proposed algorithm improves the accuracy of target localisation, and is more suitable for target location in complex environment.

关 键 词：位置信息场 目标定位粒子群优化算法极限学习机 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]