A Robust Method of Emitter Signal Deinterleaving Approach Based on Point Cloud Detection

Yifei Liu, Mingliang Tao, Shuting Tang, Jian Xie, Ling Wang

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

Abstract

Radar signal deinterleaving is a critical prerequisite process for electromagnetic situational awareness. Traditional deinterleaving approaches fail to deal with the emitter with advanced complex pulse repetition interval (PRI). In this paper, a robust deinterleaving method based on point cloud detection is proposed to estimate PRI and extract pulse sequences under complex PRI modulations. It converts pulse sequences to 2-D scatter plots which present straight lines on some specific planes by plane transformation technique. Then utilize the point cloud detection method of Random Sample Consensus (RANSAC) to extract the points on these lines corresponding to specific emitters. It can enhance the semantic features of the pulse train and reduce the sensitivity to algorithm parameters and modulation type. Experimental results demonstrate the effectiveness of the proposed method.

Original languageEnglish
Title of host publication2023 35th General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9789463968096
DOIs
StatePublished - 2023
Event35th General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2023 - Sapporo, Japan
Duration: 19 Aug 202326 Aug 2023

Publication series

Name2023 35th General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2023

Conference

Conference35th General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2023
Country/TerritoryJapan
CitySapporo
Period19/08/2326/08/23

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