Abstract: The vehicle on-board remote equipment T-box has the functions of vehicle remote diagnosis and monitoring. The accuracy of the low-cost T-box positioning receiving module is low, and it is susceptible to high-rise or elevated buildings in the city. In order to solve the locating problem of low cost locating module in T-box, a method of improving navigation precision by removing high noise data points and multiple data fusion is proposed. T-box is equipped with MIMU inertial module and OBD module, acquisition of inertial data and vehicle speed. Least squares method to fit the route of the car to remove the original positioning data of high noise latitude and longitude data points. Finally, with the car speed and inertial data to expand Karlman filter to obtain new longitude and latitude data fusion, compared with the high-precision positioning data, the method can effectively improve the low-cost T-box positioning accuracy.

Key words: Vehicle remote equipment; Low cost T-box; Fitting car route denoising; Least squares method; Extended kalman filter; Multi-Information combination; Positioning data

摘要： 车载远程设备 T-box 具有车辆远程诊断及监控等功能，低成本方案的 T-box 定位接收模 块精度低，易受城市中高楼、高架等建筑影响，出现定位点丢失或高噪声定位点。为解决低 成本 T-box 易出现的定位问题，提出了一种通过去除经纬度高噪声数据点以及多数据融合提 高导航精度的方法。T-box 带有微型惯性测量单元模块和车载诊断系统模块，分别采集微型惯 性测量单元数据和汽车速度；最小二乘法拟合汽车行驶的轨迹去除原始定位数据中高噪声经 纬度数据点；最后与汽车速度、惯性数据进行扩展卡尔曼滤波融合得到新的经纬度数据，与 高精度定位数据对比，该方法能有效提高车载远程设备定位精度。

关键词: 车载远程设备；低成本 T-box；拟合汽车路线去噪；最小二乘法；扩展卡尔曼滤波； 多信息融合；定位数据