A scheduling algorithm for space target detection of phased array radar based on cost function

Huijuan Zhao, Defeng Chen, Jian Tian, Shuyu Li

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

This paper focuses on the tracking task scheduling problem of phased array radar for space target. A task model is established and an adaptive scheduling algorithm based on cost function of tasks is proposed, which takes into account the tasks' priority and deadline, and the targets' tracking error and effective observation time. Cost factors are defined, cost function of the task is designed, and implementation steps of the algorithm are given. The algorithm is applicable when observing small and far targets, in this condition, the algorithm schedules pulses by string. The simulation results show that the scheduling algorithm proposed in this paper can effectively improve the scheduling success rate, the time use rate and the task completion ratio. The algorithm proposed can get good performance.

Original languageEnglish
Title of host publicationProceedings of the 13th IEEE Conference on Industrial Electronics and Applications, ICIEA 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1996-2001
Number of pages6
ISBN (Electronic)9781538637579
DOIs
Publication statusPublished - 26 Jun 2018
Event13th IEEE Conference on Industrial Electronics and Applications, ICIEA 2018 - Wuhan, China
Duration: 31 May 20182 Jun 2018

Publication series

NameProceedings of the 13th IEEE Conference on Industrial Electronics and Applications, ICIEA 2018

Conference

Conference13th IEEE Conference on Industrial Electronics and Applications, ICIEA 2018
Country/TerritoryChina
CityWuhan
Period31/05/182/06/18

Keywords

  • cost function
  • phased array radar
  • space target
  • task completion ratio
  • tracking task scheduling

Fingerprint

Dive into the research topics of 'A scheduling algorithm for space target detection of phased array radar based on cost function'. Together they form a unique fingerprint.

Cite this