Research On Passive Indoor Localization Fusion Technology Based On Region Refinement

Recent years,Location Based Service(LBS)has been the research hotspot,location service is attracting everyone's attention with its unique charm and playing its imperceptible role whether indoor or outdoor.With the rapid development of the Internet of Things(IoT)technology,LBS has brought more convenient experience to people's lives.At present,there are many technologies that can be applied to localization.Among them,Wi-Fi technology is more popular because it is easy to implement and cost-effective,and can be used in combination with multiple technologies.This paper analyzes a large number of Wi-Fi positioning systems and finds that some achievements have been made in the research of indoor positioning algorithms,but the precision and accuracy still need to be improved.In view of the problem,this paper uses fusion technology research and region refinement to improve the status of localization technology application.The main content of this paper as follows:(1)Aiming at the problems that the Received Signal Strength Indicator(RSSI)value is highly random,susceptible to multipath interference,and unable to characterize multipath propagation,an indoor intrusion detection and localization algorithm based on Channel State Information(CSI)has been proposed.CSI is used as the position fingerprint feature,and the collected CSI values are stored in the fingerprint database after denoising and feature extraction.At the same time,combined with the Earth Mover's Distance(EMD)algorithm to set a threshold for intrusion detection.And then estimate the general area where the target is located based on the detection results.Finally,use the Weighted k-Nearest Neighbor(WKNN)algorithm improved based on the Gaussian kernel for precise positioning.(2)Due to the high fine-grained of CSI,which contains more location information,and it is inevitable that some position features will be lost during the signal process and fingerprint match process,which will affect the improvement of localization accuracy.In order to solve the above problems and maximize the use of position features,the region refinement positioning algorithm using CSI images has been proposed.The CSI values obtained from different antennas of the same subcarrier are respectively used as RGB pixel values,and the CSI data are converted into RGB images,and then approximate positions are determined by comparing the similarity of the images.Finally,the Scale-Invariant Feature Transform(SIFT)algorithm is used to make accurate position estimation.(3)To improve the positioning accuracy further,the RSSI and CSI extracted from Wi-Fi signals are fused to use as fingerprint features,so that the coarse RSSI and finegrained CSI complement each other,and enhance the location information characteristics.So fusion indoor localization algorithm based on RSSI and CSI has been presented.Firstly,the RSSI values are used to achieve region refinement,and lever the improved k-Nearest Neighbor(kNN)algorithm to screen the reference points in the test area to build the subfingerprint database.Then,the RSSI and CSI fusion features of the reference points in the sub-fingerprint database are compared with test data.Finally,the improved WKNN algorithm combining the fusion feature is served to achieve accurate localization.