ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2017, Vol. 54 ›› Issue (5): 979-985.doi: 10.7544/issn1000-1239.2017.20160025

• 网络技术 • 上一篇    下一篇

基于KDDA和SFLA-LSSVR算法的WLAN室内定位算法

张勇1,2,李飞腾1,王昱洁1   

  1. 1(合肥工业大学计算机与信息学院 合肥 230009); 2(芜湖创业园留学人员博士后科研工作站 安徽芜湖 241000) (hfgdwhb@163.com)
  • 出版日期: 2017-05-01
  • 基金资助: 
    国家科技支撑计划项目(2013BAH52F01)

Indoor Positioning Algorithm for WLAN Based on KDDA and SFLA-LSSVR

Zhang Yong1,2, Li Feiteng1, Wang Yujie1   

  1. 1(School of Computer and Information, Hefei University of Technology, Hefei 230009); 2(Post-Doctoral Research Center of Wuhu Overseas Student Pioneer Park, Wuhu, Anhui 241000)
  • Online: 2017-05-01

摘要: 针对接收信号强度(received signal strength, RSS)的时变性降低WLAN室内定位精度的问题,提出了一种基于核直接判别分析(kernel direct discriminant analysis, KDDA)和混洗蛙跳最小二乘支持向量回归机(SFLA-LSSVR)的定位算法,该算法通过核函数策略将采集的各接入点(access point, AP)的RSS信号映射到非线性领域,有效提取了非线性定位特征,重组定位信息,去除冗余定位特征和噪声;然后采用LSSVR算法构建指纹点定位特征数据与物理位置的映射关系模型,采用SFLA算法优化该关系模型的参数,并用该关系模型对测试点的位置进行回归预测.实验结果表明:提出算法在相同的采样次数下的定位精度明显优于WKNN,ANN,LSSVR算法,并且在相同的定位精度下,采样次数较大减少,是一种性能良好的WLAN室内定位算法.

关键词: 接收信号强度, 无线局域网, 室内定位, 核直接判别分析, 混洗蛙跳算法, 最小二乘支持向量回归机

Abstract: The time-varying received signal strength (RSS) degrades the indoor positioning accuracy in wireless local area network (WLAN). A novel indoor positioning algorithm based on kernel direct discriminant analysis (KDDA) and shuffled frog leaping algorithm and least square support vector regression (SFLA-LSSVR) is proposed to address the problem. Firstly the proposed algorithm employs kernel function strategy to map RSS signal to the field of nonlinear, which is sampled from each access point (AP), and extracts nonlinear features effectively, and reconstructs the positioning information, and discards the redundant positioning features and noise. Secondly, LSSVR algorithm is employed to build the mapping relation model between positioning features and physical locations, and SFLA is employed to optimize the parameters of the relation model, and then test points locations are predicted by using the relation model. Experimental results show that the positioning accuracy of the proposed algorithm is much superior to WKNN, ANN, LSSVR algorithm under the condition of the same sampling numbers, and the number of RSS signal which is sampled from each AP is significantly reduced in the same positioning accuracy, and the proposed algorithm is a WLAN indoor positioning algorithm with good performance.

Key words: received signal strength (RSS), wireless local area network (WLAN), indoor positioning, kernel direct discriminant analysis (KDDA), shuffled frog leaping algorithm (SFLA), least square support vector regression (LSSVR)

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