TOA Estimation And Location Based On 5G Millimeter Wave Signal

With the continuous development of information technology,location services have been widely used in many fields such as smart cities,elderly care,emergency rescue,and intelligent warehousing.The satellite positioning system can satisfy most outdoor positioning needs,but the positioning ability in indoor areas where human activities are frequent is weak.With the advent of the 5G era,the technology represented by millimeter wave communication will be widely used.Compared with other signals,millimeter-wave signals are characterized by high bandwidth,short wavelength,and sparse channel,which can provide more accurate TOA estimation for Time of Arrival(TOA)positioning and provide high-precision positioning for indoors.New technology approach.Therefore,this paper studies the TOA estimation and localization algorithm applied to indoor 5G millimeter waves.The main work of the thesis is divided into the following parts:1.In the traditional compressed-sensing millimeter-wave TOA estimation algorithm,the delay of solving the error element increases the problem of system time overhead.This paper proposes a millimeter-wave TOA estimation algorithm based on the maximum path gain for compressed sensing estimation.According to the condition that the path gain of the path of the TOA is large,the algorithm eliminates the elements that are not satisfied by the traditional compressed sensing algorithm,and the minimum delay of solving the remaining elements is the TOA value.Finally,the algorithm is verified by simulation.When the Signal to Noise Ratio(SNR)is-5dB,the time consumption of the algorithm is reduced by 48.1%compared with the traditional compressed sensing estimation TOA algorithm.2.In the localization process,the traditional particle filter-based map matching algorithm uses the straddle test method to detect whether the particle wears through the wall or not,which leads to the problem that the system takes a long time.This paper proposes a map matching algorithm based on the two-dimensional map information matrix,which takes the indoor continuous map.Discretization of information generates a two-dimensional map information matrix,and the particle is penetrated through the wall according to the two-dimensional map information matrix,thereby reducing the computation time of the traditional particle filter-based map matching algorithm.Finally,when the number of particles is 500,the algorithm is verified by simulation.The results show that the map matching algorithm based on two-dimensional map information matrix reduces the time consumed by the system by 51.9%compared with the traditional particle filter-based map matching algorithm.3.Finally,this paper implements the simulation of indoor fusion localization algorithm based on millimeter wave TOA and two-dimensional map information matrix,and performs simulation verification when the SNR is-5dB and the number of particles is 500.The results show that the proposed indoor homing method is reduced by 15.5%compared with the multi-base station weighted fusion localization algorithm based on TOA and Angle of Arrival(AOA).