针对室内、地下以及障碍物较多的复杂环境中,可用导航源匮乏问题,本文提出一种利用低频时变磁场实现目标高精度位置与姿态解算的解决方案。传统时变磁场定位方法要求磁信标坐标系与目标坐标系一致且无法解算目标相对姿态角信息,同时精...针对室内、地下以及障碍物较多的复杂环境中,可用导航源匮乏问题,本文提出一种利用低频时变磁场实现目标高精度位置与姿态解算的解决方案。传统时变磁场定位方法要求磁信标坐标系与目标坐标系一致且无法解算目标相对姿态角信息,同时精度普遍较差。本文提出的方案在解决传统方案的局限性基础上,又提出一种基于指纹匹配的改进方案,具有穿透性好、鲁棒性强且精度高的特点。首先根据空间中测量磁场计算磁信标接收信号强度RSSI(Received Signal Strength Indicator)拟合直线,根据指纹匹配原理估计目标位置;再根据测量磁场方向矢量模型,反演解算目标姿态角信息,实现目标位置与姿态信息解算过程,研究并分析了磁信标导航系统误差来源及解决方案;最后通过对比实验,验证本文提出的算法在实验条件下,位置估计误差期望为0.069 m,姿态角估计误差期望为2.3°,且误差不随时间积累,相对于传统的磁信标导航方案具有明显优势,具有较高的工程应用价值。展开更多
Although k-nearest neighbors （KNN） is a popular fingerprint match algorithm for its simplicity and accuracy, because it is sensitive to the circumstances, a fuzzy c-means （FCM） clustering algorithm is applied to i...Although k-nearest neighbors （KNN） is a popular fingerprint match algorithm for its simplicity and accuracy, because it is sensitive to the circumstances, a fuzzy c-means （FCM） clustering algorithm is applied to improve it. Thus, a KNN-based two-step FCM weighted （KTFW） algorithm for indoor positioning in wireless local area networks （WLAN） is presented in this paper. In KTFW algorithm, k reference points （RPs） chosen by KNN are clustered through FCM based on received signal strength （RSS） and location coordinates. The right clusters are chosen according to rules, so three sets of RPs are formed including the set of k RPs chosen by KNN and are given different weights. RPs supposed to have better contribution to positioning accuracy are given larger weights to improve the positioning accuracy. Simulation results indicate that KTFW generally outperforms KNN and its complexity is greatly reduced through providing initial clustering centers for FCM.展开更多
文摘针对室内、地下以及障碍物较多的复杂环境中,可用导航源匮乏问题,本文提出一种利用低频时变磁场实现目标高精度位置与姿态解算的解决方案。传统时变磁场定位方法要求磁信标坐标系与目标坐标系一致且无法解算目标相对姿态角信息,同时精度普遍较差。本文提出的方案在解决传统方案的局限性基础上,又提出一种基于指纹匹配的改进方案,具有穿透性好、鲁棒性强且精度高的特点。首先根据空间中测量磁场计算磁信标接收信号强度RSSI(Received Signal Strength Indicator)拟合直线,根据指纹匹配原理估计目标位置;再根据测量磁场方向矢量模型,反演解算目标姿态角信息,实现目标位置与姿态信息解算过程,研究并分析了磁信标导航系统误差来源及解决方案;最后通过对比实验,验证本文提出的算法在实验条件下,位置估计误差期望为0.069 m,姿态角估计误差期望为2.3°,且误差不随时间积累,相对于传统的磁信标导航方案具有明显优势,具有较高的工程应用价值。
文摘Although k-nearest neighbors （KNN） is a popular fingerprint match algorithm for its simplicity and accuracy, because it is sensitive to the circumstances, a fuzzy c-means （FCM） clustering algorithm is applied to improve it. Thus, a KNN-based two-step FCM weighted （KTFW） algorithm for indoor positioning in wireless local area networks （WLAN） is presented in this paper. In KTFW algorithm, k reference points （RPs） chosen by KNN are clustered through FCM based on received signal strength （RSS） and location coordinates. The right clusters are chosen according to rules, so three sets of RPs are formed including the set of k RPs chosen by KNN and are given different weights. RPs supposed to have better contribution to positioning accuracy are given larger weights to improve the positioning accuracy. Simulation results indicate that KTFW generally outperforms KNN and its complexity is greatly reduced through providing initial clustering centers for FCM.
