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计算机系统应用:2018,27(8):237-240
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基于LS_SVM的超宽带定位算法
程启国
(江南大学 轻工过程先进控制教育部重点实验室, 无锡 214122)
Ultra-Wideband Location Algorithm Based on LS_SVM
CHENG Qi-Guo
(Key Laboratory of Advanced Process Control for Light Industry(Ministry of Education), Jiangnan University, Wuxi 214122, China)
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投稿时间:2017-12-12    修订日期:2018-01-04
中文摘要: 针对超宽带在非视距环境下测距误差大引起定位误差较大的问题,提出了一种基于最小二乘支持向量机支持向量机(Least Squares_Support Vector Machine,LS_SVM)算法的测距误差处理.该方法将室内区域划分为多个相等的小区域,建立每个区域的采样信号的特征值和节点位置的非线性关系,利用LS_SVM对其进行分类和回归进行定位,对于非视距测距结果赋予较小的一个权重.实验仿真表明,相比K邻近算法(K-Nearest Neighbors,K-NN)误差精度在7 cm内提高10%,说明本算法能够有效的提高定位精度.
中文关键词: 超宽带  定位  支持向量机  误差消除
Abstract:Designated to solve the problem of large positioning error caused by large ranging errors in the non-line-of-sight (UWB) environment, a tracking error based on Least Squares Support Vector Machine (LS_SVM) is proposed. The method divides the indoor area into several equal small areas, establishes the non-linear relationship between the eigenvalues of the sampled signals and the node locations in each area, classifies and regulates them by using LS_SVM. For non-line-of-sight distance measurement results, a smaller weight is given. Experimental results show that the error of K-Nearest Neighbors (K-NN) is improved by 10% within 7 cm, which shows that this algorithm can effectively improve the positioning accuracy.
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基金项目:江苏省产学研联合创新资金前瞻性联合研究项目(BY2014023-31);江苏省“六大人才高峰”项目(WLW_007)
引用文本:
程启国.基于LS_SVM的超宽带定位算法.计算机系统应用,2018,27(8):237-240
CHENG Qi-Guo.Ultra-Wideband Location Algorithm Based on LS_SVM.COMPUTER SYSTEMS APPLICATIONS,2018,27(8):237-240

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