Non-line-of-Sight Recognition Algorithm Based on Deep Learning

Zhang, Chengjun; Wang, Hongmei; Wang, Zhiwei; Wang, Faguang; Min, Minghui; Li, Shiyin

doi:10.1007/978-981-99-0416-7_66

Cited by 1 publication

(2 citation statements)

References 8 publications

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“…The U and C values in this state are the final membership matrix and the cluster center. Combined with the research of Chen et al [11], it is proved that this method can effectively identify LOS and NLOS.…”

Section: Pca-fcm Recognition Algorithmmentioning

confidence: 79%

“…In summary, it is found that U and C are interrelated and contain each other. At the beginning of the algorithm, there is neither U nor C. First, a value is assigned to one of the variables, and then another variable is obtained according to Formulae (11) and (12). In this way, U and C are continuously iterated and updated, towards the objective function J(U, C) at a decreasing direction, and finally, a steady state is reached.…”

Section: Pca-fcm Recognition Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Research on DL-TDOA Positioning Performance Optimization in Tunnel Scenarios

You,

Tian,

Gao

et al. 2023

J. Phys.: Conf. Ser.

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High-precision vehicle positioning is a necessary condition for realizing intelligent transportation and autonomous driving, and positioning information is one of the basic elements to ensure the security of the Internet of Vehicles business. Aiming at the high-precision positioning requirements of tunnel scenarios, the DL-TDOA technology is analysed and optimized in this paper. First, the non-line-of-sight (NLOS) error suppression algorithm is optimized, then the influence of the deployment vertical and horizontal distance ratio of base station nodes on the positioning accuracy in the actual deployment is analysed, and finally, the clock accuracy and output frequency of the device are improved. Through the optimization above, the positioning accuracy of DL-TDOA technology in the tunnel environment can be enhanced, and the application effect can be improved.

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Section: Pca-fcm Recognition Algorithmmentioning

confidence: 79%

Section: Pca-fcm Recognition Algorithmmentioning

confidence: 99%