Back

Identification Method of Vehicle Key Performance Parameters based on PSO Algorithm.

  • Published In: International Journal of Vehicle Structures & Systems (IJVSS), 2023, v. 15, n. 3. P. 380 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Qingfeng Liu; Lewen Feng; Jianhong Guo 3 of 3

Abstract

In order to improve the identification effect of vehicle key performance parameters, this paper applies the PSO algorithm to the identification of vehicle key performance parameters and focuses on analyzing the parameters of the vehicle key performance parameter monitoring equipment and the propagation environment. The improvement of sensitivity of the monitoring receiver can effectively extend the coverage distance. NLOS is a ubiquitous transmission environment and changing NLOS to LOS propagation further extends the coverage distance. In order to ensure the communication quality of radio communication users, ensure that the service area covered by radio waves and the reliability of radio wave propagation, this paper calculates the propagation loss from the receiving antenna to the transmitting antenna in detail. The experimental results show that the identification method of vehicle key performance parameters based on PSO algorithm proposed in this paper can play an important role in the identification of vehicle key performance parameters. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Vehicle Structures & Systems (IJVSS). 2023/04, Vol. 15, Issue 3, p380
  • Document Type:Article
  • Subject Area:Communication and Mass Media
  • Publication Date:2023
  • ISSN:0975-3060
  • DOI:10.4273/ijvss.15.3.17
  • Accession Number:173426088
  • Copyright Statement:Copyright of International Journal of Vehicle Structures & Systems (IJVSS) is the property of Carbon Magics Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.