The Research Of INS Correction Method Based On Map-matching

With the increasing precision of digital map, the road network information fromdigital map can provide reliable positioning reference. Map-matching algorithm is tomatch anchor points provided by navigation system with the use of digital roadnetwork information. GPS is controlled by the U.S. military and it can’t be usedduring the war. At the same time，it is unable to guarantee the normal work incomplex urban environment because of its vulnerability to the shield of the tallbuilding, Not only the reliability of map matching positioning is secured with theimprovement of digital map accuracy, but also the autonomy of the inertial navigationsystem is guaranteed. According to the map matching results, drift errors of theinertial navigation system can be suppressed in turn. Therefore, the inertial navigationsystem and the map-matching algorithm are complementary to each otherappropriately.The existing map-matching algorithms have some problems in real-time,matching accuracy, robustness, etc. That’s why it is very necessary to develop aspecialized map-matching algorithm for INS data.The main work undertaken by this thesis is as following:The initial anchor point error region is selected with the method of two staggeredgrid, under the condition of ensuring the matching accuracy. The real-time of themap-matching algorithm is improved by reducing the error area.Addressed to the low matching precision of the projection matching method, theprojection distance is redefined in this paper and the credibility of the distanceinformation from the algorithm is improved. At the same time, depending on thedifferent road conditions, the weights of distance and angle are selected by adaptivemethod and the adaptability of the map-matching algorithm to various complicatedroad conditions is improved.The error of the inertial navigation system is corrected by using Kalman filteringmethod as well as the information of matching points so that the inertial navigationsystem can work in a stable state for long periods.