基于MCB的传感网移动目标定位算法  

Mobile Object Localization Algorithm for Sensor Networks Based on MCB

作　　者：张锐恒[1] 庄毅[1] 赵振宇[2] 王洲[2] 顾晶晶[1] 

机构地区：[1]南京航空航天大学计算机科学与技术学院,南京210016 [2]中国航空工业洛阳电光设备研究所,洛阳471009

出　　处：《计算机科学》2012年第8期34-37,共4页Computer Science

基　　金：航空科学基金(2010ZC13012)资助

摘　　要：针对MCB移动定位算法在实时信息采样方面的不足,提出了EMCB(Enhanced Monte-Carlo localizationBoxed)定位算法。该算法在MCB算法基础之上,引入遗传算法中的变异和交叉操作,使样本选择向后验密度值较大的区域转移,从而有效地解决了原算法存在的成功采样率较低的问题。仿真实验结果表明,同MCB算法相比,EMCB算法平均采样数减少了约30%,定位精度提高了约17%。In view of low real-time sampling efficiency of Monte-Carlo localization boxed,a new localization method named enhanced Monte-Carlo localization boxed was proposed.Based on the MCB,EMCB introduces the crossover and mutation operations in genetic algorithm to make samples to move towards regions with large value of posterior density distribution.So the distribution of samples is optimized,and the problem of low sampling efficiency is solved.Simulation results show that compared with the MCB,the new algorithm reduces the number of samples.Therefore,the sampling efficiency and localization accuracy are improved by about 17%,while the cost is reduced by about 30%.

关 键 词：无线传感器网络 移动定位 蒙特卡洛 遗传算法(GA) 

分 类 号：TP393.1[自动化与计算机技术—计算机应用技术]