摘要
iBeacon有着体积小、功耗低、覆盖范围广等特点,因此其在室内定位领域有着非常明显的优势。文章提出了一种基于iBeacon的以定位点RSSI与参考点RSSI的欧氏距离倒数的归一化值作为权值的改进型KNN算法,同时提出了一种空间滤波算法,提升了算法效率。仿真实验结果表明,文章提出的改进型KNN算法在定位精度上相比原KNN算法提升了43.6%。此外,文章提出的空间滤波算法提高算法的运算速度约34%,使得在保证定位精度的同时,保证了定位的实时性。
iBeacon has the characteristics of small size, low power consumption and wide coverage, so it has a very obvious advantage in the field of indoor positioning. This paper proposes an improved KNN algorithm based on the normalized value of the Euclidean distance reciprocal of location point RSSI and reference point RSSI, and proposes a spatial filtering algorithm to improve the efficiency of the algorithm. The simulation results show that the improved KNN algorithm proposed in this paper improves the positioning accuracy by 43.6% compared with the original KNN algorithm. In addition, the spatial filtering algorithm proposed in this paper has a speed of about 34%, which ensures the positioning accuracy while ensuring the real-time performance of the location.
出处
《无线互联科技》
2018年第6期113-115,共3页
Wireless Internet Technology
