Optimization Of Radio Map Based On Deconstruction Of Positioning Signal

Indoor location technology can provide users with real-time location information in indoor scenes.It is one of the basic technologies of indoor location-based services,and also the key research content in the field of navigation and positioning.With the popularization of information technology in public places,large-scale wireless access points(AP)are usually deployed in large public indoor scenes.Because the Wi Fi signal transmitted by APs has a long transmission distance and is easy to measure,the fingerprint positioning technology based on Wi Fi has become one of the mainstream indoor positioning technologies.However,a large number of APs laid to ensure the quality of communication,while providing multi-dimensional information to the positioning system,also bring the problems of offline fingerprint data complexity and slow computing speed.In some cases,unstable APs may reduce the accuracy of the positioning system.Based on this,this paper starts with the optimization of AP and reference point(RP)fingerprint acquisition,and takes the optimization of offline fingerprint map for indoor positioning of Wi Fi fingerprint as the research content to seek the quality improvement strategy of offline fingerprint map to improve the comprehensive performance of the positioning system.The main research work of this paper is as follows:(1)Aiming at the problems of high data storage cost,slow operation speed and low positioning accuracy brought by large-scale AP in large public indoor places,as well as the shortcomings of existing AP selection methods,such as difficulty in determining the optimal number of AP and low degree of redundancy removal,this paper proposes an AP selection and multicluster matching method based on information interaction.The algorithm calculates the global AP information gain under a single RP to consider the information value of each AP at that RP,expresses the correlation,redundancy and interactive information characteristics of multiple APs through the fusion of interactive weight factors,updates the interactive weight of the candidate AP set and the preferred AP set,and ensures that the final AP set is the optimal AP set.Based on the similarity of the optimal AP set between RPs,a cross-class fuzzy region is established at the boundary of the sub-class with similar APs to achieve the multi-class matching of the online target to be located extending to the adjacent edge class,and the final location result is obtained.The simulation results show that compared with other classical AP filtering algorithms,the proposed algorithm can effectively reduce the amount of data storage and improve the positioning accuracy.(2)Aiming at the regional change of path loss index in complex indoor environment,a database expansion and indoor localization method based on fingerprint definition obtained by signal-fluctuation is proposed.The algorithm combines the density peak clustering,and through fingerprint definition matching on the sampling reference point,realizes the description of the sub-region characteristics with the signal fluctuation degree,and constructs the difference nearest neighbor propagation model to avoid the inaccurate prediction of the signal value of the target point by the traditional signal loss model.In the online stage,the Euclidean and fingerprint definition distance between the map and the signal vector to be located is comprehensively considered,and high-quality RPs with high discrimination are selected to improve the positioning accuracy of the fingerprint database.Compared with collecting global RPs data,the proposed scheme reduces a lot of labor costs and time costs.The simulation results show that the proposed scheme can provide higher positioning accuracy than other classical fingerprint database expansion and RP optimization algorithms,and has higher application value in indoor environment.(3)Aiming at the problem that offline fingerprint will gradually fail over time,a fingerprint map verification and restoration algorithm based on signal classification is proposed.The algorithm realizes the quantitative classification of online observed fingerprints through the sub-uniform quantization function.After the hot area and thermal identification and offset detection of the overall positioning results of the target area,it determines whether the AP is abnormal based on the difference of the observed fingerprints of a single AP at all levels in combination with the chi-square test.The fingerprint map is updated through the secondary fingerprint acquisition strategy of low quality AP removal and subdivision according to the hot area offset.Simulation experiments show that the proposed algorithm can effectively link the fingerprint failure caused by AP anomalies and natural environment changes.