基于支持向量机多分类的室内定位系统

为解决室内实时定位中定位精度不高、显示效果来回跳动的问题,提出了一种基于支持向量机(SVM)多分类的室内定位算法。针对传统基于采样点的匹配算法处理非线性问题的不足以及实时定位时信号采集时间较短、变化幅度较大等问题引入网格定位的概念,将定位匹配设计成多分类问题,利用SVM得到目标最有可能所属的K个网格;利用实时定位中前、后两个位置的相关性剔除这K个网格中可能性较小的网格,最终所属网格坐标加权后得到估算位置坐标,并利用卡尔曼滤波算法对估算位置坐标进行滤波处理。实验结果表明,算法的定位精度与传统SVM的精度相比有明显的提高。

Multi-classification Algorithm for Indoor Positioning Based on Support Vector Machine

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.