Multi-network auxiliary positioning method for distributed large-scale multi-antenna system

Abstract

The invention provides a multi-network auxiliary positioning method for a distributed large-scale multi-antenna system. A diffraction model used for feature collection is introduced into RSS-based DBN positioning, the new RSS features effectively consider the influence of other UE on target UE, and the adaptability of a trained DLBP network in a multi-user scene is improved; the motion trail of a UE is analyzed by utilizing LSTMN under the condition that only a small amount of trail information exists, the complete trail of the UE can be effectively obtained by utilizing a DBN estimation result and a small amount of position samples, and compared with a traditional method, the historical trail information acquisition and measurement cost is greatly reduced; and according to the method, the estimation results of the DBN and the LSTMN are combined through the BPNN, and high positioning precision can be achieved.

Description

Technical field [0001] The present invention relates to the field of wireless communication, positioning, and machine learning technologies, in particular to a multi-network aided positioning method for distributed large-scale multi-antenna systems. Background technique [0002] With the development of the Fifth Generation, 5G) network, the location information of the terminal equipment can be used to provide regional advertisements, content caches, and emergency people's tracking services, which prompted wireless user positioning technology to become an important aspects and industry. One of the research directions [1] Zhang Ziyan, Huang Jin'an, Cai Zihua. Analysis of the development trend of 5G communication positioning integrated network [J] .Pran Communication Technology, 2019,39 (02): 45-49. Currently, common outdoor communication systems are mainly used to obtain location information of the terminal based on the Global Positioning System (GPS). However, since satellite sign...

AI Technical Summary

Problems solved by technology

Nevertheless, the existing solutions [17][18][19][20] require a large amount of historical trajectory information to estimate the next location of the target UE, so it requires high data measurement costs, which is not conducive to practical applications

[0007] In the real environment, with the continuous improvement of users' requirements for positioning accuracy, the training database is also getting larger and larger, which brings additional preliminary work to data collection, so it is difficult to adapt to the rapidly changing scene and high sampling cost. Application scenarios [21] T.Liu, Y.Yang, G.Huang, Y.K.Yeo, and Z.Lin, Driver distraction detection using semi-supervised machine learning [J], IEEE Transactions on Intelligent Transportation Systems, vol.17, no.4 , pp.1108–1120, Apr.2016

For example, in an indoor environment, the movement of indoor facilities such as furniture and objects and the movement of people will cause multipath and shadow effects in signal propagation, resulting in strong time-varying characteristics of RSS, which makes it impossible to analyze the same location after the training data set is collected. The sampling data can be reused for a long time

In the absence of regular update and maintenance, this will cause the problem that the positioning results gradually deviate from the accurate value [22] Li Yanjun, Xu Kaifeng, Shao Jianji. Research on the method of updating Wi-Fi indoor positioning fingerprint database by crowdsourcing [J]. Journal of Sensitive Technology, 2014(12):108-114

Traditional DL-based positioning algorithms do not fully consider the impact of mobile personnel on the RSS of target users in multi-user scenarios, fail to make full use of trajectory information, and require high sampling data collection costs, so they cannot fully meet the positioning needs of real environments. DL-based The positioning algorithm still needs further research

Images