Research On Indoor Positioning Method Based On Gaussian Process Model

In recent years,people's demand for location services is also increasing.With the development of wireless networks,the application of wireless location technology is more and more widely used.Especially in indoor scenes where people frequently move,such as parking lots,shopping malls,tourist attractions,more precise and low-cost location services are needed.The indoor positioning method based on Gauss process model in this paper can improve positioning accuracy under the premise of reducing energy consumption,and has important application significance.Firstly,this paper studies the status quo of wireless location technology at home and abroad,expounds the advantages of wireless network in positioning,compares several commonly used location algorithms based on ranging,analyses their respective application scope,advantages and disadvantages,and analyzes several factors affecting positioning accuracy,and several typical propagation models.It is concluded that the positioning method based on RSSI value is less affected by the environment and has better positioning accuracy.However,traditional positioning methods often consume manpower and material resources in the process of obtaining RSSI value database.In this paper,the Gauss process model is used to predict RSSI value to reduce energy consumption.Firstly,some data are collected to train the Gauss process model,and the optimization algorithm is used to train the super parameters in the Gauss process model.By comparing the experimental data,it can be concluded that the positioning effect is better when the particle swarm optimization algorithm is used to train the Super-parameters in the Gaussian process model.The estimated RSSI database of the area to be measured is generated by using the Gaussian process model.The real-time RSSI values are compared with the estimated map database,and the estimated location of the point to be measured is obtained.Experiments show that the localization algorithm proposed in this paper can obviously improve the positioning accuracy,and the localization algorithm has good stability and can adapt to different environments.If the algorithm is improved according to the use environment and specific purpose,it can also be applied to other positioning systems,such as intelligent warehouse management system,intelligent tracking monitoring system,etc.