摘要
针对传统蒙特卡罗定位算法由于节点采样效率低导致的定位精度低、定位不准确的缺陷,提出一种改进的最小二乘拟合蒙特卡罗(LSFMCL)定位算法。该算法利用MBC算法优化采样空间,并利用最小二乘拟合节点运动轨迹,对节点位置进行预测,进一步得到最优采样区域,最后提出权值概念并利用预测节点的权值信息计算未知节点的位置。仿真结果表明,与传统算法相比,优化后的算法提升了节点的采样率,提高了定位精度,对于移动节点的定位具有更加广泛的应用前景。
The traditional Monte Carlo localization algorithm has the defects of low positioning precision and inaccurate positioning due to the low efficiency of node sampling.Therefore,an improved least squares fitting Monte Carlo localization(LSFMCL)algorithm is proposed.The algorithm is adopted to optimize the sampling space by taking advantage of MBC algorithm,fit the node motion trajectory with least square method to predict the location of nodes,and then obtain the optimal sampling area.The weight concept is proposed to calculate the location of the unknown node by using the weight information of the predicted node.The simulation results show that,in comparison with traditional algorithm,the optimized algorithm can improve the sampling rate of the node and positioning accuracy,and has wider application prospect for the localization of the mobile nodes.
作者
谭志
梁丽文
夏磊
TAN Zhi;LIANG Liwen;XIA Lei(College of Electrical and Information Engineering,Beijing University of Civil Engineering and Architecture,Beijing 100044,China)
出处
《现代电子技术》
北大核心
2018年第15期10-15,共6页
Modern Electronics Technique
基金
北京市教育委员会科技发展计划面上项目(KM201110016015)
北京市属高等学校人才强教计划项目(PHR201008374)~~
