Design And Application Of Indoor Location Algorithm Based On WiFi Location Fingerprint

Outdoor positioning technology is now quite mature.Global satellite navigation systems,including Beidou satellite navigation system and GPS,combine inertial devices or prior data and electronic maps to perform optimal estimation and matching to obtain more accurate position estimates.However,due to the limitations of its technical principles,positioning cannot be performed in an indoor environment.With the introduction of concepts such as smart cities and smart campuses,indoor navigation and positioning technology has become an indispensable part.The current indoor positioning technology is mostly low-power short-distance positioning.The wireless WiFi-based positioning technology has natural advantages in implementation and deployment.The hardware equipment requirements are low,and the positioning environment only needs a wireless AP.Therefore,the WiFi location Fingerprint information positioning algorithm is the research direction.Considering that a single positioning method is easily affected by various uncertain factors,the fusion algorithm of indoor positioning technology has become the trend of future research.As a prediction algorithm suitable for various dynamic systems containing unknown information,Kalman filter is It is widely used in various motion-related predictions including inertial navigation and aerospace.It can accurately predict the next state of the system in a continuously changing dynamic system from a series of incomplete and noise-containing measurements.Therefore,the Kalman filter can also be used in the positioning algorithm to accurately predict the specific location of the positioning user.With accuracy and stability as the evaluation conditions of the algorithm,combined with the Kalman filter algorithm and the adaptive weighted K-nearest neighbor algorithm,a fusion positioning algorithm based on WiFi location fingerprints is proposed,and the simulation system is designed for algorithm testing.The average error is about 1.270 meters,and the positioning results will not produce abnormal fluctuations.And the algorithm is applied to the analysis of regional crowd concentration,which can count the flow of people in time periods,and present the real-time crowd gathering in the form of heat map.