Topology Optimization of Bistatic Radar for Target Location Based on Conditional Entropy Lower Bound

Chunxia Li*, Shude Zhou, Tao Zeng, Quanhua Liu, Mingxing Li, Renjian Lv

*Corresponding author for this work

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

Abstract

As a new kind of detection technology, bistatic radar has attracted more and more attention. The target location accuracy depends on the topology configuration of bistatic radar. To improve the target location accuracy, it is necessary to optimize the configuration of the bistatic radar system. In this paper, a method for optimal configuration of bistatic radar system based on the conditional entropy lower bound (LB) of bistatic radar is proposed. Firstly, the conditional entropy and its LB of the target position estimate in given bistatic radar system's polar observations are deduced theoretically. Then, the optimal topology configuration of bistatic radar is obtained by minimizing the conditional entropy LB. Finally, the effectiveness of this method is verified by simulation results.

Original languageEnglish
Title of host publicationIEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798331515669
DOIs
Publication statusPublished - 2024
Event2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024 - Zhuhai, China
Duration: 22 Nov 202424 Nov 2024

Publication series

NameIEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

Conference

Conference2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024
Country/TerritoryChina
CityZhuhai
Period22/11/2424/11/24

Keywords

  • bistatic radar
  • conditional entropy
  • lower bound
  • target location
  • topology configuration

Fingerprint

Dive into the research topics of 'Topology Optimization of Bistatic Radar for Target Location Based on Conditional Entropy Lower Bound'. Together they form a unique fingerprint.

Cite this