Research On Indoor Wireless Location Algorithm Basing On Spherical Antenna Array

The growing business demand of Location Based Serve(LBS)has promoted the development of many location estimation technologies in recent years.LBS is widely used in shopping malls,hospitals,warehouses,parking lots and other occasions,and has broad application prospects in flow monitoring,medical assistance,cargo allocation and so on.On the one hand,the location estimation accuracy is seriously affected by the complex and changeable environment and multipath effect.On the other hand,the deployment difficulty and density of the location estimation system are increased because of the demand for three-dimensional spatial location estimation such as shopping malls.Therefore,location estimation technology and efficient system deployment adapted to three-dimensional space environment become the focus of research.In this thesis,the antenna structure and position estimation algorithm of position estimation system,which are the key factors affecting the accuracy of position estimation,are studied.In this thesis,a GSLS(Gradient-Based Simplified Least Squares)method is proposed for angle estimation,and the method is applied to spherical antenna position estimation system to realize three-dimensional(3D,Three Dimension)spatial position estimation.The main contents of this thesis include the following aspects:Firstly,the existing TSLS(Three-Stage Simplified Least Squares)method is improved,and the GSLS method is proposed to realize angle estimation.TSLS method realizes angle estimation based on sector antenna,but its AOA(Angle of Arrival)estimation process needs to constantly adjust the weight of angle fusion(DFU,DoA Fusion)according to the change of environmental signal-to-noise ratio to achieve the minimum AOA estimation error,which is cumbersome and difficult to achieve in practice.The proposed GSLS method replaces the Direction of Arrival estimation and fusion steps in TSLS method by constructing a joint probability density function between received signal strength(RSS)and AOA,reduces the influence of weight on the accuracy of AOA estimation in DFU process,and reduces the error of AOA estimation.At the same time,GSLS uses iterative method to solve the optimal estimation,and non-iterative method in TSLS.It is easier to converge to the optimal estimation result and has higher accuracy than AOA estimation.Secondly,the GSLS method is introduced into the spherical antenna space positioning system to realize the AOA estimation process based on the spherical antenna.In order to verify the effectiveness of the system,the AOA estimation and position estimation performance of spherical antenna in free space and multi-path indoor space are analyzed through theoretical analysis and simulation experiments.The results show that under the condition of relative sparse deployment(K<5),the mean error of indoor space position estimation of spherical antenna position estimation system is 3.29M at 100m*100m*20m,the mean error of horizontal position estimation is 2.69m,and the mean error of AOA estimation is 6.76deg.The mean error of direction angle,azimuth and AOA are 1.16%,2.85%,respectively.The error of A estimation and location estimation is low,which can meet the positioning requirements of open indoor space such as gymnasium and large warehouse.Thirdly,aiming at indoor positioning requirements,AOA estimation results based on spherical antenna are proposed as fingerprints for position estimation.Based on the position estimation experiments in typical environments,it is concluded that AOA fingerprint localization is insensitive to device heterogeneity but sensitive to environmental disturbances.The simulation results show that the fingerprint location estimation method based on OA is insensitive to environmental disturbance and equipment heterogeneity,and the accuracy of location estimation is 24.7%higher than that of fingerprint location estimation method based on RSS.At the same time,because the vector dimension of AOA fingerprint is smaller,the time-consuming of database fingerprint matching in location estimation is reduced by 25%,and the real-time performance of location estimation is better.