Research On Universal Location Of Large Indoor Environment

With the development of pervasive technologies and the continuous improvement of the performance of mobile terminal devices,user location information is widely used,and the demand for user location information service(LBS)is increasing.Satellite navigation and positioning services can provide better positioning services outdoors,but in an indoor environment,satellite signals are difficult to be detected through buildings to reach indoors,and cannot meet the user's indoor location information service requirements.Therefore,indoor positioning technology has become a research hotspot of current location information services.Indoor positioning based on Wi-Fi and geomagnetism does not require the deployment of additional equipment.Users only need to have smart devices to achieve positioning,so these two methods have become hot spots in indoor positioning.In a large indoor environment,it is difficult to provide a good location information service using a single geomagnetic positioning technology or Wi-Fi technology.A single Wi-Fi signal is difficult to provide users with accurate location information and a single geomagnetic signal requires a lot of manpower and material resources to build a fingerprint library in a large indoor environment,and when the fingerprint library is too large,the positioning time of the positioning stage will be greatly increase.Therefore,for the positioning in a large indoor environment,this article proposes to combine the two,specifically including the following two aspects:(1)First,in a large environment,design an indoor room-level positioning algorithm based on the text classification of the Wi-Fi fingerprint library.The traditional Wi-Fi-based fingerprint library matching method has a large positioning error due to the unstable Wi-Fi signal strength.Therefore,this paper introduces a short text method to convert Wi-Fi signal strength into short text words,and then Wi-Fi fingerprint library into short text data set,using text classification algorithm for indoor room-level positioning,using 98 mall data(10 groups)Train and test this method,the positioning accuracy is maintained at about 97%,and compared with other methods,the positioning time is significantly reduced.The test results show that the proposed positioning method can effectively improve the positioning accuracy and reduce the positioning time,and achieve better positioning performance.(2)After room-level positioning based on Wi-Fi,accurate room location information is obtained,and more stable geomagnetic positioning is used in the room to obtain more accurate location information.Therefore,an indoor geomagnetic positioning algorithm based on integrated learning and BP neural network is designed In view of the problems of unstable geomagnetic signals and large geolocation errors caused by geomagnetic fingerprints in the current indoor geomagnetic positioning technology,this paper introduces the BP neural network model as a weak predictor,and integrates multiple sets of weak predictors into strong through integrated learning methods.Predictor.Using the UJIIndoorLoc-Mag data set as the test set,11 paths were experimentally tested with a positioning error of 2.64 meters.Using the personally collected data set as the test set,7 paths were tested through the experiment with a positioning error of 0.92 meters,indicating the proposed The positioning method can effectively reduce the positioning error.Figure[5,6]table[13,14,15]reference[48,49,50]...