3D Reconstruction of Target Geometry Based on 2D Data of Inverse Synthetic Aperture Radar Images

Wang, Xin; Guo, Baofeng; Shang, Chao-xuan

doi:10.3724/sp.j.1146.2013.00140

Cited by 5 publications

(1 citation statement)

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“…Therefore, Zuo et al [23] used the SIFT and speeded up robust feature (SURF) algorithms to extract and match the feature points. In reference [24], the association between the 2D scattering centres in different attitudes is completed based on the Kalman filter and the nearest neighbour data association criterion. Because of the anisotropy of electromagnetic scattering, some scattering centres only exist in specific ISAR images and cannot be distinguished and extracted in other ones.…”

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Three‐dimensional point cloud reconstruction of inverse synthetic aperture radar image sequences based on back projection and iterative closest point fusion

Wang

et al. 2022

IET Radar Sonar & Navi

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In order to recover the three-dimensional (3D) structure of the target from sequential inverse synthetic aperture radar (ISAR) images, the factorisation method is generally used. It requires a large number of high-quality matched feature points from different ISAR images. If the number of extracted feature points is insufficient, the restored 3D structure is not obvious. Furthermore, the mismatching of feature points will greatly affect the quality of target reconstruction. However, the factorisation method only uses the information from the ISAR images, while that from imaging geometry is not sufficiently considered. ISAR imaging is a kind of projection, and the projection plane information could be taken into account for the 3D reconstruction. Hence, a new 3D reconstruction method for stable targets from sequential ISAR images is proposed in this paper. Firstly, the ISAR images are preprocessed with the CLEAN algorithm. The maximum betweencluster variance method is applied to extract feature points from the processed images. Moreover, the image projection plane and projection equation corresponding to different ISAR images are analysed by using imaging geometry information. According to the projection equation, the feature points are back-projected (BP) to the 3D space. Finally, the 3D point clouds obtained by the BP from multiple radar images are fused by the iterative closest point algorithm to restore the 3D structure of the target. The simulation and experiment results show the effectiveness and robustness of this method. K E Y W O R D Sback project (BP), feature extraction, inverse synthetic aperture radar (ISAR), iterative closest point (ICP), maximum between-cluster variance method (OTSU) | INTRODUCTIONInverse synthetic aperture radar (ISAR) can generate twodimensional (2D) images for non-cooperative moving targets and be used for many military purposes [1]. The ISAR image is the projection of the target on the Range-Doppler (RD) plane. The target information is inevitably lost in the projection process. In order to achieve more abundant and stable characteristic information, one important direction is to reconstruct the three-dimensional (3D) structure from ISAR images.According to the imaging system difference, the current ISAR 3D imaging methods can generally be divided into three categories. The first one is the single-antenna ISAR 3D imaging method. One of the approaches is the sum-difference beam 3D imaging. The monopulse radar receiver is provided with four channels, which can form the sum channel signals and difference channels in the horizontal and pitch directions. Aiming at the target with multiple scattering points, the 3D image can be obtained by applying coherent accumulation and angle measurement technology [2][3][4][5]. Although the sumdifference beam 3D imaging principle is relatively simple, the method is generally only suitable for short-range targets since the lateral resolution is poor under limited angle measurement accuracy for long-distance targets. The second method isThis is an ...

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confidence: 99%