Research on Using Digital Map of Car Navigation System
|Course||Measuring Technology and Instrument|
|Keywords||GPS/DR car navigation Digital map MapX Mapinfo Map-matching|
For the last decade, much work has been concentrated on car navigation systems. The first one developed merely gave the car position in the text form.However,recently developed systems give the shortest route as well as the car position on a graphic screen.These systems are expected to communicate with other traffic management systems to get traffic information nearly.Most car navigation systems estimate the car position from the dead reckoning and the Global Positioning System (GPS).However, in combining the GPS and dead reckoning, the Kalman filter can be used.The estimated position by the Kalman filter is proved to be optimal if the system is linear and the noise is white Gaussian.It should be noted that the GPS signal is corrupted by selective availability noise (S/A noise) for civil users, the characteristic of S/A noise is unknown, the noise of GPS signal is not white Gaussian, so the estimate position from the Kalman filter is not optimal. It leads to position error.To reduce the error, many integration methods have been suggested. Map matching is an useful approach to correct the error, as the reliable road maps are readily available. The integrated navigation and position system, composed of GPS/DR and map matching technology, is applied. In this text, visual C++ and the high precision road network information in the digital map database(mapinfo\mapx) is used as the moulding board to revise positioning errors of the receiving datum. According to the result of this parten recognition process, the exact location will be displayed on the digital map. The experiments for operating vehicles demonstrate that the degree of accuracy and reliability of integrated navigation and positioning system, compared with GPS/DR navigation and positioning system, is enhanced, vehicular operation efficiency and security are far and away strengthened and the thoroughfare capacity is improved. As consequence, traffic congestion can be moderated with effect and the automation of transportation management and the intelligence of the vehicular drive will be realized.The matching structure of the map matching subsystem is then discussed in detail.Regarding to every model , this paper gave detail methods.A prediction method is used to predicting the next position of the vehicle.These model improve the system efficiency.