Research And Application Of Adaptive Indoor Positioning Method In Dynamic Environment

The demand in indoor localization have been increasing,many Internet of Things(IOT)applications use different technologies to provide indoor localization services.Because of the lower deployment cost,higher universality and the good performance of localization accuracy,the fingerprint localization technology based on Bluetooth Received Signal Strength(RSS)has attracted extensive attentions.However,in a large-scale dynamic localization environment,the poor stability of RSS signals and a large amount of offline fingerprint data result in low localization accuracy and high computational complexity of the Bluetooth-based fingerprint localization system.In order to solve the above problems,this paper improves the RSS-based fingerprint localization method.It is important to realize the correction of signal fluctuation value by predicting the path-loss characteristics in partition.In order to improve the positioning performance of the algorithm,this paper constructs the partition model and location matching model of the positioning scene.Finally,a Bluetooth fingerprint positioning system based on the improved positioning algorithm has been build.The content of the full text mainly consists of the following parts:(1)RSS signal has poor stability in large-scale positioning scenes.Considering that,indoor location algorithm has relatively low accuracy and efficiency.This paper proposes a signal two-scale nearest neighbor with dynamic correction algorithm(DGC-TSNN).According to the connectivity structure of the target area,the algorithm uses Support Vector Machine to construct a partition model of the target area.This paper trains AP signal-distance model through Gaussian process regression in partition.It is important to realize the correction of signal fluctuation value by predicting the path-loss characteristics in partition.In order to improve positioning accuracy,a two-scale nearest neighbor algorithm is established by merging signal similarity with signal differences.The K value of nearest neighbor is adaptively calculated by combining the environmental parameter in order to reduce the influence of environmental noise.Compared with other algorithm,DGC-TSNN algorithm has significantly improved positioning performance.(2)In order to reduce the signal interference between the beacons and the signal difference of the physical partition,this paper proposes probability weighted fusion method based on One-vs-Rest Support Vector Machine(OVRSVM)and Multivariate Gaussian Naive Bayesian model(MVGNB),which is marked as WSGM.To reduce the signal difference caused by physical partition,this method uses improved K-Means algorithm to cluster and partition fingerprint data.The method uses Thompsom test theory to check the physical coordinates of the reference points in partition,which can eliminate abnormal points.The proposed method establish a conditional probability function based on multivariate Gaussian naive Bayesian model,which can reduce the mutual interference between the signal strength of access point.Experiments show that compared with other algorithm,WSGM algorithm has obvious advantages in positioning stability and accuracy.(3)The paper designs and implements an indoor positioning system based on Bluetooth Low Energy(BLE),which includes a Bluetooth beacon,a Bluetooth gateway and a mobile terminal positioning APP.The Bluetooth beacon is used for signal broadcasting in the area.The Bluetooth gateway realizes the calibration of different types of equipment.The mobile terminal positioning APP realizes the functions of offline fingerprint collection,online positioning and location navigation.In the offline stage,the offline fingerprint collection function completes the establishment of the fingerprint database of the positioning area.The online positioning is divided into device calibration and real-time positioning.According to the linear calibration rules,the system realizes the calibration of different devices.In the online stage,the DGC-TSNN and WSGM algorithms are used to estimate the user position.The navigation service implements navigation path planning based on the user's input location and current location.