| 5G communication technology plays an important role in daily life in the future.One of the important indicators is that the transmission rate is nearly 10 times higher than that of 4G communication technology.However,the current wireless communication technology mostly works in the lower frequency band,and the lower transmission rate is difficult to meet the requirements of high transmission rate.In addition,due to the limited spectrum resources in low frequency band,researchers have begun to search for more abundant bandwidth resources.At present,the millimeter wave channel of the 60 GHz band with a bandwidth of nearly 7 GHz has received great attention.With the acceleration of urbanization,more and more comprehensive service buildings are built,and people's indoor activities become more frequent.The interior has gradually become the main scene of people's daily life.Therefore,many daily work and life services urgently require accurate estimation of indoor target location information.Based on the above background,this paper studies how to estimate indoor target position using 60 GHz millimeter wave frequency band.First of all,according to the beamforming technology,the antenna array gain can be provided,which can be used to compensate for the transmission loss and increase the transmission distance.Secondly,considering in the IEEE 802.15.3c standard,the phase of the codeword in the codebook-based beamforming scheme is limited,and the beamforming technique is shortened according to the detailed hierarchical scanning proposed in the standard proposal of IEEE 802.11.ad.In this paper,an adaptive algorithm based on codebook beamforming and target position information estimation is proposed.Firstly,the azimuth angle of the target is obtained by using the codeword phase of the optimal beam,and the estimation of the target position is obtained by calculating the azimuth angle of the target using multiple base stations.Then,when the target position is larger than the angle between the base station and the optimal codeword,the target position estimate and the angle of the base station are used to update the codeword phase.Finally,when the difference between the two angles is less than the set threshold value,the iteration is stopped.The simulation results show that compared to the LS algorithm,the above adaptive algorithm through codebook and target position information has a higher error cumulative distribution function.Secondly,in view of the limitation of single location algorithm,this paper further studies the fusion of two or more location algorithms to improve the accuracy of location.The traditional Okeine-Ostmann positioning data fusion model is based on the TOA/TDOA measurement values.Considering the angle information obtained by beamforming in the 60 GHz millimeter wave,the original Okeine-Ostmann data model is improved.The angle information obtained by beamforming,the target position estimation values obtained by the TOA+AOA and TDOA+AOA hybrid positioning algorithms are fused.The simulation results show that the improved fusion model can get the target position estimate closer to the actual target position.However,the method of directly using mean and variance to select the estimated value of the target location has great errors.Finally,because the fusion algorithm of the decision layer in the data fusion model directly determines the target position estimation value after the fusion,it affects the accuracy of the target position estimation value to a large extent.In this paper,an improved density-based clustering algorithm is proposed.By choosing the region with the highest density of the estimated target location as the region where the actual target location is located,the mean of the estimated position in this region is taken as the estimated value of the actual target location.The simulation results show that the density-based clustering algorithm can reduce the positioning error and improve the positioning accuracy to a certain extent. |
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