Enhanced Radar Target Detection with Local Minimum Entropy CLEAN

Hao Liu, Chen Yao, Guoqiang Zhao*, Xingyu Chen

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

Abstract

In the case of low signal-to-noise ratio (SNR), cross term interference generated by time-frequency analysis of multiple targets echo signal are difficult problems in radar detection. In order to reduce the errors caused by cross terms, an improved CLEAN algorithm based on the local minimum entropy criterion is proposed in this paper. The algorithm uses the local minimum entropy of the spectrum to improve the estimation accuracy of sinusoidal parameters, and combines with the CLEAN algorithm to improve the multiple targets detection capability. The simulation results show that the CLEAN algorithm based on the local minimum entropy criterion can detect all the targets when multiple targets interfere with each other.

Original languageEnglish
Pages (from-to)1864-1869
Number of pages6
JournalIET Conference Proceedings
Volume2023
Issue number47
DOIs
Publication statusPublished - 2023
EventIET International Radar Conference 2023, IRC 2023 - Chongqing, China
Duration: 3 Dec 20235 Dec 2023

Keywords

  • CLEAN algorithm
  • cross term interference
  • local minimum entropy

Fingerprint

Dive into the research topics of 'Enhanced Radar Target Detection with Local Minimum Entropy CLEAN'. Together they form a unique fingerprint.

Cite this