Research And Implementation Of WLAN Indoor Positioning Technology Based On Visual Assistance

Indoor location services based on smart handheld mobile terminals have attracted great attention from researchers due to their convenience,efficiency,and considerable market prospects.High-precision indoor positioning has gradually become a basic requirement for people's daily lives.The indoor environment is complex and changeable.How to achieve adaptive,universal and high-precision indoor positioning in a dynamic indoor environment is the focus and difficulty of current research.The indoor positioning method based on Received Signal Strength Indicator(RSSI)is widely used because of its simple equipment and easy processing of data.However,the wireless signal will be affected by obstacles during the transmission process,which will greatly reduce the strength of the received wireless signal,and then affect the positioning accuracy.The human body compensates for the loss caused by the wireless signal transmission in a dense indoor environment.The technical route is to adopt the log-normal path loss model as the theoretical model of RSSI,and on this basis,a new wireless signal compensation model is proposed by comprehensively considering population density and wireless signal frequency.The model first uses convolutional neural networks to perform human body detection on visual image information,and calculates the number of individuals in the current indoor environment.Then,the model will convert the wireless signal propagation loss caused by the human body into corresponding loss compensation.Finally,according to the received wireless signal strength,indoor positioning is achieved through the principle of multilateral positioning.Through simulation and testing in three indoor environments with different intensities,the results are better in real-time and higher in positioning accuracy than the positioning results that do not consider the influence of human body on wireless signal propagation.