Algorithm And Application Of Indoor Positioning Based On WIFI Location Fingerprint

With the rapid development of the Internet of Things industry,the information exchange among wireless sensor networks,mobile terminals and wearable devices is becoming more and more frequent,which greatly changes people's lifestyle and also promotes the growing demand for indoor positioning.However,due to the complex and changeable indoor environment and the increasing user experience requirements,indoor positioning technology is facing the enormous challenges.At present,there is no perfect indoor positioning method,which can not only meet the requirements of positioning accuracy,but also ensure positioning performance.In this paper,we studied the WIFI location fingerprint technology combined with spatial-temporal geographic weighted regression model and compressive sensing.The main contents include:1)A new algorithm for constructing RSSI location fingerprint database is proposed,which integrates geographic and temporally weighted regression model of collaborative training.Based on geographic and temporally weighted regression model we embedded the propagation path loss and several environmental impact factors into the model as explanatory variables,and then used the collaborative training method to improve the accuracy and environmental adaptability of the model,so as to reduce the fingerprint collection density and shorten the collection time.2)A compressive sensing localization model based on improved region partitioning algorithms.The candidate RSSI sequences of localization are obtained by the improved Bisecting K-means algorithm,which divides the area into several clusters and then construcs the observation matrix for the compressed sensing localization model.Then,the original measurement matrix is processed by the improved compressive sensing localization model to preprocess the data,recover the target signal and calculate the localization coordinate.3)The indoor positioning technology based on WIFI location fingerprint is applied.We designed and implemented a recommendation system for non-scoring and non-consuming stores based on WIFI location information,which provides real-time indoor positioning service and shop recommendation function.Additionally,we compared the proposed fingerprint database construction algorithm with existing similar algorithms through experiments.The results show that this method improves the accuracy of RSSI fingerprint construction and can adapt to the changes of various environments.At the same time,we compared the proposed localization algorithm with the existing localization algorithm through experiments.The results show that the proposed algorithm improves the localization performance and achieves better localization accuracy and stability in the same environment.