3D ISAR Imaging: The Alignment Problem

Jinjian Cai, Marco Martorella, Quanhua Liu, Zegang Ding, Elisa Giusti

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

6 Citations (Scopus)

Abstract

Inverse synthetic aperture radar (ISAR) is capable of producing two-dimensional (2D) and three-dimensional (3D) images of noncooperative targets. Compared to 2D ISAR images, 3D ISAR reconstruction can provide not only range and cross-range information, but also the height information of the target, which is of great significance for automatic target recognition (ATR). The alignment between the point-like 3D ISAR reconstructions of targets and the targets' models, such as CAD models, becomes one of the essential issues in ATR by 3D ISAR reconstruction. In this paper, we introduce an approach to address the proposed alignment problem. The alignment problem can be decomposed into two steps, a coarse alignment and an accurate alignment. The coarse alignment can be accomplished by means of the principal component analysis (PCA), while the accurate alignment can be achieved by iteration closest points (ICP) algorithm. In order to simulate real radar scenarios, target self-occlusion and reconstruction errors are taken into consideration. The simulation results verify the validity of the proposed methods.

Original languageEnglish
Title of host publication2019 International Radar Conference, RADAR 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728126609
DOIs
Publication statusPublished - Sept 2019
Event2019 International Radar Conference, RADAR 2019 - Toulon, France
Duration: 23 Sept 201927 Sept 2019

Publication series

Name2019 International Radar Conference, RADAR 2019

Conference

Conference2019 International Radar Conference, RADAR 2019
Country/TerritoryFrance
CityToulon
Period23/09/1927/09/19

Keywords

  • 3D ISAR reconstruction
  • CAD model
  • Inverse synthetic aperture radar (ISAR)
  • iteration closest points (ICP)
  • point cloud alignment
  • principal component analysis (PCA)

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