The most common location algorithms based on received signal strength （RSS） are location identification based on dynamic active radio frequency identification （LANDMARC） and virtual reference elimination （VIRE）....The most common location algorithms based on received signal strength （RSS） are location identification based on dynamic active radio frequency identification （LANDMARC） and virtual reference elimination （VIRE）. However, both the original algorithms suffer from some drawbacks. In this paper, several aspects of the two original algorithms have been modified to reduce the positioning errors. Firstly, Lagrange interpolation has been used instead of linear interpolation. Secondly, adaptive threshold has been introduced in the new algorithm. Thirdly, insert virtual reference tags to improve the location accuracy of the boundary of the sensing area. Finally, combine LANDMARC with VIRE to absorb both advantages. Compared with the original algorithms, on average, simulated results show that the modified algorithms can improve the location performance efficiently and achieve the goal of accurate positioning in indoor environment.展开更多
由于室内环境存在严重干扰,导致经典室内定位算法LANDMARC(location identification based on dynamic active RFID calibration)在定位目标时出现选错参考标签的概率增大;此外,还需计算待定位标签和每个参考标签之间的欧氏距离,因而具...由于室内环境存在严重干扰,导致经典室内定位算法LANDMARC(location identification based on dynamic active RFID calibration)在定位目标时出现选错参考标签的概率增大;此外,还需计算待定位标签和每个参考标签之间的欧氏距离,因而具有较高计算复杂度。针对以上的缺点,提出了一种改进的双标签LANDMARC定位算法,通过定义双标签,即一个有源标签和一个无源标签,来定位目标标签的定位模型,该算法命名为DLANDMARC。由于无源标签被感应的距离有限,只能被处在它附近的待定位标签感应到,从而大大降低选错参考标签的概率并减小了计算开销。实验表明,DLANDMARC算法在定位精度、定位时间以及算法的稳定性比文献中已有的几种算法有明显改善。展开更多
基金supported by the National Natural Science Foundation of China (61003237)
文摘The most common location algorithms based on received signal strength （RSS） are location identification based on dynamic active radio frequency identification （LANDMARC） and virtual reference elimination （VIRE）. However, both the original algorithms suffer from some drawbacks. In this paper, several aspects of the two original algorithms have been modified to reduce the positioning errors. Firstly, Lagrange interpolation has been used instead of linear interpolation. Secondly, adaptive threshold has been introduced in the new algorithm. Thirdly, insert virtual reference tags to improve the location accuracy of the boundary of the sensing area. Finally, combine LANDMARC with VIRE to absorb both advantages. Compared with the original algorithms, on average, simulated results show that the modified algorithms can improve the location performance efficiently and achieve the goal of accurate positioning in indoor environment.
文摘由于室内环境存在严重干扰,导致经典室内定位算法LANDMARC(location identification based on dynamic active RFID calibration)在定位目标时出现选错参考标签的概率增大;此外,还需计算待定位标签和每个参考标签之间的欧氏距离,因而具有较高计算复杂度。针对以上的缺点,提出了一种改进的双标签LANDMARC定位算法,通过定义双标签,即一个有源标签和一个无源标签,来定位目标标签的定位模型,该算法命名为DLANDMARC。由于无源标签被感应的距离有限,只能被处在它附近的待定位标签感应到,从而大大降低选错参考标签的概率并减小了计算开销。实验表明,DLANDMARC算法在定位精度、定位时间以及算法的稳定性比文献中已有的几种算法有明显改善。
