用于车辆分类的多传感器车型特征融合算法

Multi-Sensor Signature Fusion Algorithm for Vehicle Type Classification

为避免单一地磁传感器进行车型识别时存在的误差,结合自主研发的地磁传感器网络,提出一种基于多传感器的车型特征融合算法.该算法通过计算传感器网络内不同车型特征信号的相关关系来确定车辆运行状态,并利用最大似然法则结合皮尔逊相关系数进行数据融合,以获取更加准确的车辆特征信号,为现有车型分类方法提供更加准确的输入参数.实际道路试验表明,与现有单节点分类算法相比,文中算法对中大型车辆的分类准确率可以提高17.5%.

In order to avoid the error of vehicle type classification with single-geomagnetic sensor,a geomagnetic sensor network is constructed and is used to create a multi-sensor signature fusion algorithm for vehicle type classification. In this algorithm,the statuses of moving vehicles are determined on the basis of correlations of different vehicle signatures,and vehicle data are fused via the maximum likelihood in combination with the Pearson correlation coefficient ,so that more accurate vehicle signatures are acquired and more precise inputs for vehicle type classification are provided. Practical road experiments show that,in comparison with the existing single-sensor classification algorithms,this newly proposed algorithm helps the classification accuracy for medium-or large-size vehicles improve by 17 . 5%.