一种改进的基于双标签的LANDMARC定位算法

Improved LANDMARC localization algorithm based on double-tag

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。

Due to the severe interference in the indoor environment, the probability of selecting the wrong reference tag of the classical indoor localization algorithm LANDMARC increases when locating a target. In addition, LANDMARC needs to calculate the Euclidean distance between a tag to be located and a reference tag, which results in a high computation complexity. To solve these two shortcomings, this paper proposed an improved double-tag LANDMARC localization algorithm, namely DLANDMARC. The proposed DLANDMARC defined double tags which combind an active tag and a passive tag, to locate the target tag positioning model. Since the induction distance of a passive tag was limited, so the probability of selecting wrong reference tags was decreased and the computational time was thus reduced. Experiment results show that DLANDMARC has a better performance in terms of the localization accuracy, the computational time and the stability compared with other existing algorithms in the literature.