Research On Indoor Combined Positioning Based On WiFi/UWB/ Barometer

With the rapid development of indoor location services,indoor mobile positioning technology has gradually become one of the research hotspots.In indoor complex scenarios,achieving high-precision,low-cost positioning still faces more challenges.In order to satisfy the need of low-cost,high-precision of indoor personnel position,researches on WiFi positioning and ultra-wideband positioning are carried out.Finally,a WiFi/UWB/barometer combination positioning scheme is proposed,combining the advantages of different technologies.The main contents are as follows:(1)The WiFi fingerprinting was studied and the effects of different fingerprint library optimization algorithms,different localization algorithms,and different density fingerprint distributions on positioning accuracy were compared.The analysis shows that after using the Gaussian filter and the k-means algorithm to optimize the fingerprint database,the positioning effect is improved overall.The positioning accuracy of the positioning algorithm depends not only on the size of the K value in the nearest neighbor algorithm,but also on the area where the positioning point is located.When the fingerprint point is close to the edge of the fingerprint database,the positioning effect is poor.The higher the density of the fingerprint distribution is,the higher the positioning accuracy is.(2)The basic method of UWB positioning is studied,and a combined filtering algorithm based on NLOS judgment is designed to improve the positioning accuracy under the mixed scenes of line-of-sight and non-line-of-sight.In the aspect of ultra-wideband positioning,an ultra-wideband simulation and experiment system based on LabVIEW is designed.It is concluded that reasonable base station deployment can significantly reduce the positioning error.(3)A WiFi/UWB combined positioning algorithm is proposed.UWB ranging is used to obtain an improved K-nearest neighbor algorithm(UWB-KNN)and an improved weighted K-nearest neighbor algorithm(UWB-WKNN).The improved algorithm first uses the ultra-wideband ranging value to filter the WiFi fingerprint points,and after the matching and positioning,the positioning result is added into the neighbor set as a new neighbor.The UWB-KNN algorithm finds the nearest neighbor to the UWB base station among these neighbors,and uses the position coordinates corresponding to this neighbor as the final positioning result.The UWB-WKNN algorithm uses the distance value between the nearest neighbor and the UWB base station to reassign the neighbors,and then calculates the final positioning result.Through experimental comparison and analysis,it is concluded that UWB-KNN algorithm and UWB-WKNN algorithm can significantly improve the accuracy of static positioning and dynamic positioning.(4)A WiFi/UWB/barometer combined positioning scheme was designed.Firstly,the fingerprint database acquired offline is optimized by the k-means algorithm;then,the WiFi signal strength value received by the terminal to be positioned is filtered,the ultra-wideband ranging value is corrected by the error compensation model,and then the UWB-is used.The KNN or UWB-WKNN algorithm calculates the coordinates;finally,the air pressure gauge is used to measure the height to obtain the corresponding floor,and the position of the target to be positioned in the building is obtained.The algorithms of WiFi positioning and UWB positioning are optimized respectively in this paper.And a WiFi/UWB algorithm,combining the advantages of the two positioning technologies,is proposed.Compared with the traditional WiFi positioning,it has higher accuracy.With the help of barometers,the height of people and finally the 3D coordinates in the building can be easily obtained.This technology has a good application prospect in indoor positioning and related industries.