Research On Indoor Positioning Method Based On Model Iteration

With the development of artificial intelligence and security monitoring,the research of indoor positioning is also deepened continuously.It has become a hot topic.However,the traditional indoor positioning method has the problems of low ranging accuracy,low positioning accuracy,and unstable positioning results.Based on the machine learning algorithm,this thesis studies the signal ranging model,indoor positioning model and indoor location information fusion.Aiming at the problem of low accuracy of ranging,this paper proposes a GBDT ranging model.An indoor positioning method based on GBDT model iteration is proposed.In addition,proposes weighted ratio and threshold method.Aiming at the problem of instability indoor positioning and the problem of singularity in positioning results,we design a positioning information fusion algorithm.Firstly,A ranging model based on the machine learning GBDT algorithm is proposed.According to the idea of data mining and the propagation law of the signal in the environment,the signal feature is extracted as the model input and the propagation distance of the signal is the output.The variance and extreme values that reflect signal fluctuations are introduced as the input features of the ranging model to assist in distance measurement.The machine learning boosting algorithm GBDT is first time used to establish the ranging model.The experimental results show that the proposed ranging model has higher accuracy than those in recent years.Secondly,an iterative method is proposed to achieve indoor positioning based on the GBDT ranging model.Arranging Zig Bee transmitters at known locations in the room and taking the receiver which needs to be located at unknown location.Based on the idea of fingerprint positioning,the iterative GBDT indoor positioning model through features and output can be built where the signal propagation distance calculated by the GBDT ranging model is one of the features and the vertical and horizontal coordinate values of the unknown node are the output of the GBDT positioning model respectively.In order to further improve positioning accuracy,the weights based on signal strength and thresholds are used to improve the effect of points with large signal intensity on positioning results.The experimental results show that the indoor positioning method based on model iteration has better positioning accuracy.Finally,based on Zig Bee and the GBDT iterative method,an information fusion with a high positioning accuracy sensor positioning method is proposed.A positioning method combining Zig Bee and RFID sensors is designed to further improve the accuracy of indoor positioning by employing the RFID positioning technology to assist large individual errors in the GBDT iterative method.In summary,due to the complex indoor environment,the signal information at different locations cannot be categorized by the same parameter.Therefore,combined with the fingerprint positioning and ranging model,an indoor positioning method based on model iteration is proposed to describe the environmental information at different locations more accurately.To further improve the positioning accuracy and improve the stability of the positioning results,the information of Zig Bee sensor and RFID sensor are integrated.