| With the expanding public demand for indoor location services,indoor positioning technology has become an international research hotspot in the field of navigation and location services.However,existing indoor positioning technologies are still difficult to meet the positioning needs of complex indoor environments,and there are two main technical problems and challenges:First,the interference of various uncertainties in the complex indoor environment for positioning signals leads to a decrease in the accuracy and stability of indoor positioning solutions.Second,the performance of existing indoor positioning algorithms still has much room for improvement.To address the above problems,the mainstream solutions are mainly exploring new sensor technologies for localization and multi-sensor fusion localization schemes.However,new sensor positioning technologies are often accompanied by high costs,complex equipment installation,and high-power consumption.So,they cannot be widely promoted and applied.Multi-sensor fusion positioning solutions mainly use Wi-Fi positioning technology as the basic positioning solution,supplemented by sensors or wireless technologies such as low-power Bluetooth,gyroscope,cellular network,magnetometer,etc.for positioning correction.In this paper,based on the current demand for positioning performance of indoor positioning services and the accurate grasp of the similarities and differences between Wi-Fi technology and Bluetooth low energy technology,we conduct an in-depth study on the fused indoor positioning scheme based on Wi-Fi fingerprint positioning technology and the ranging technology of Bluetooth low energy.The main work of this paper is as follows:First,we constructed a Wi-Fi fingerprint database with the underground garage of North China Electric Power University as the experimental scenario,and explored the distance characteristics,time characteristics and distribution characteristics of RSSI variation of Wi-Fi routers in indoor environment.The different signal characteristics performance of 2.4GHz band and 5GHz band signals in the indoor environment are analyzed through experiments,and the optimal Wi-Fi fingerprint localization effect of the mixed band is verified.Meanwhile,for the basic positioning scheme,this paper experimentally explores the positioning results of different matching algorithms in the online positioning stage of Wi-Fi fingerprint.Secondly,based on the acquisition of low-power Bluetooth signals in the underground garage of North China Electric Power University and the logarithmic path loss model of wireless signals,we completed the construction of a distance measurement model for Bluetooth low energy with the least squares fitting technique,and also determined the selection principle for distance data with the experimental results of actual distance measurement errors.Considering the cost of equipment and acquisition,spatial and temporal flexibility,and repeatability of experiments,this paper designs a simulation model of Bluetooth low energy signals based on actual data,and combines the ranging model to obtain a large amount of simulated physical distance data.Finally,we propose a graph optimization model based on the g2o framework to complete the construction and solution of the total error function in the graph optimization model.In this paper,we introduce the information matrix and Huber kernel function to weaken the influence of error in the optimization process of localization results,and make integral position adjustment of the user node population based on the principle of affine transformation to solve the graph drift problem caused by the lack of absolute coordinates in the graph optimization solution process.We experimentally explore the effects of the Huber kernel function threshold,the number of nodes in the graph,node distribution and different Wi-Fi fingerprint matching algorithms to verify the effectiveness of the fused indoor localization algorithm based on the graph optimization model proposed in this paper. |
PDF Full Text Request