Multi-classification Algorithm for Indoor Positioning Based on Support Vector Machine
基于支持向量机多分类的室内定位系统

A multi-classification algorithm for indoor positioning based on SVM was proposed to tackle the problem of low precision and fluttering results faced in many real-time location systems. Traditional matching algorithms based on sampling points arc always deficient in dealing with nonlinear problem and jumping results in a short time. In handing this limitation,object location process was considered as a multi-classification problem by introducing grid concept K candidate grids were obtained using SVM first These candidates were then refined by previous location results, and ultimate accuracy result was achieved through a Kalman filter. Temporal information was utilized in the matching process to make the object movement more stable and smooth. Experiments show the superiority of our method over naive SVM method.