Research On High Precision Indoor Positioning Method Based On Map Matching

Currently,there is just an increasing demand for indoor location services.However,due to the complicated indoor environment and serious signal loss,many existing indoor location technologies fail to meet people's requirements for positioning accuracy.Existing improved positioning methods such as fusion positioning,collaborative positioning,additional aids such as map matching,PDR and so on can improve the positioning accuracy.As the map has the natural binding effect on the positioning result,it is an indispensable factor for high-precision indoor positioning.Therefore,more and more scholars begin to study the use of map matching technology to modify the positioning point.Map matching technology was originally proposed to be used to correct the positioning results in outdoor environment,but the indoor positioning technology is more and more attention.Map matching technology has also begun to be used in the correction of indoor positioning error.At present,many popular indoor map matching methods are based on Bayesian models,but because of the computational complexity of this map matching method,the real-time positioning is poor.However,the traditional topological map matching method,which is relatively simple to calculate,has relatively large matching errors owing to less consideration.Therefore,this paper proposes a topological map matching algorithm based on multiple weights and a map navigation model oriented to map matching,and combines the navigation model and map matching algorithm to improve the matching accuracy.First of all,in order to obtain the map information,and the map information can be conveniently used,this paper presents the idea of using the classification information for indoor map modeling.The idea divides indoor map information into nine categories of elements,each of which constitutes its components and two map models are formed by combining different components.One is the map-oriented map navigation model,which contains all topology information and indoor constraint information in the map.The purpose is to reduce the computational complexity of map matching algorithm and improve the matching accuracy of map.The second is a classification of nestable three-dimensional indoor map model,the model is mainly to address the lack of existing indoor elevation map information,and reduce the cost of map updates.Secondly,this paper presents a topological map matching method based on multiple weights.Based on the traditional topology map,this method adds a weight based on historical selection information.The selection weight is used to record the number of times each path in the map is selected by the pedestrian,and when using the weight in the indoor scene where only a few paths are frequently selected,the probability of correctly matching the path can be increased.The weight coefficients of map matching is also optimized,and the weight coefficients suitable for different scenes are calculated through preprocessing so as to achieve the effect that the map matching algorithm can achieve better accuracy under different scenarios.The path matching point is also optimized.Instead of using the nearest point of the anchor point and the path as the matching correction point,the moving acceleration of the pedestrian is comprehensively considered to determine the anchor point correction position on the path.Finally,this paper validates the map-oriented map navigation model and the topological map matching method based on multiple weights.A typical scenario is used to verify that the proposed map navigation model and multi-weight topological map matching method are available and effective.