多传感器与道路网数据用于汽车导航的研究

Multiple Sensors and Road Network Used for Mobile Navigating

对车载导航系统中GPS接收机、车轮计数器和电子罗盘传感器数据与导航电子地图所提供的道路网数据进行了综合处理 ,建立了道路网数据的网络拓扑关系 ,在此基础上提出了基于道路网络拓扑关系 ,利用GPS接收机、车轮计数器和电子罗盘传感器对汽车进行实时定位的算法。最后 ,对导航中汽车的定位误差进行了分析。实验证明 ,GPS接收机、车轮计数器和电子罗盘传感器数据与道路网数据综合 ,在导航过程中能够补偿GPS信号丢失 ,实时、高精度、高可靠性地确定汽车的位置。

This paper presents a real_time positioning algorithm for mobile navigating based on topology of road network and sensors of GPS receiver,electronic compass,wheel counter.Through mobile navigating system experiments,it proves that this algorithm can solve signal loss of GPS receiver and acquire satisfied precision,high reliability and real_time result. At present,GPS receiver is the major sensor in mobile navigating system.The positioning precision of single GPS can only reach 30～50 m.Due to the limited cost,it is impossible to process the signal of GPS by real_time difference to obtain high positioning precision.Because of the effect of GPS positioning error,when the car is running along the bridge,it may be positioned in the river.So the navigating precision is poor. On the other hand,the principle of GPS is by receiving at least four satellite signals at the same time.Because the high buildings or the effects of other signals,such as electric field,magnetic field,etc.block the GPS signals,the number of received satellites by GPS receiver is less than four.So the GPS receiver can not work normally. Based on the above case,only using GPS as positioning sensor is not enough in mobile navigating system.Other restriction conditions should be considered in order to get better navigation results.At present,the main methods of improving positioning precision are difference GPS and integration GPS with inertia navigation.Both methods can implement high precision navigation.However,the cost of navigation system is very high,and it is not practical for mobile navigation system.This paper does in_depth research on road net data and the sensors of GPS receiver,electronic compass,and wheel counter in mobile navigating system.