Three-Dimensional Geometry Reconstruction Method for Slowly Rotating Space Targets Utilizing ISAR Image Sequence

Abstract

For a slowly rotating space target (SRST) with a fixed axis, traditional 3D geometry reconstruction methods become invalid as the projection vectors cannot be formed without accurate target rotational parameters. To tackle this problem, we present a new technique for 3D geometry reconstruction by using inverse synthetic aperture radar (ISAR) image sequence energy accumulation (ISEA). Firstly, by constituting the motion model of SRST, an explicit expression is derived to describe the relative geometric relationship between the 3D geometry and ISAR image sequence. Then accurate rotational parameters and the 3D geometry of SRST can be estimated by combining the idea of the ISEA method and quantum-behaved particle swarm optimization (QPSO). Compared with the ISEA method, which can be only applied to triaxial stabilized space targets, the proposed method can achieve 3D geometry reconstruction of SRST. Experimental results based on the simulated point model and simulated electromagnetic computer aided design (CAD) model validate the effectiveness and robustness of the proposed method.

Joint estimation of imaging plane and three‐dimensional structure based on inverse synthetic aperture radar image sequences

“…Finally, 3D space point coordinates are constructed to obtain the 3D mesh of the target. Compared to [6], it can optimize the imaging plane vectors and obtain the 3D mesh of the target, and the surface information is more complete and accurate.…”

“…Through the manual method, Zhou et al. [6] matched a small number of stable scattering points to construct the observation matrix, which could decompose the imaging plane vectors by FM. Finally, the EA method in [5] was used to obtain the 3D point clouds.…”

“…However, the imaging plane vectors are difficult to estimate effectively for non-triaxial stabilized targets, so that this method is no longer applicable. Through the manual method, Zhou et al [6] matched a small number of stable scattering points to construct the observation matrix, which could decompose the imaging plane vectors by FM. Finally, the EA method in [5] was used to obtain the 3D point clouds.…”

Parametric scene model for geometry restoration using differential rendering method

“…Deep learning techniques can be employed for 3D reconstruction tasks. These networks can learn to estimate depth or generate 3D representations directly from 2D images 4,5 . Perhaps the most interesting and promising approaches are those utilizing learning-based methods 6 .…”

Recognition of Micro-Motion Jamming Based on Complex-Valued Convolutional Neural Network

“…With the increasing complexity of electromagnetic environment, electronic warfare plays an important role in modern warfare [ 1 ]. Inverse synthetic aperture radar (ISAR) is widely used in modern radar warning systems (RWS), which provides high-resolution situation-awareness information for combat [ 2 , 3 , 4 , 5 , 6 , 7 ]. Noteworthy, the widely developed micro-motion feature extraction technology [ 8 , 9 , 10 , 11 , 12 ] makes it possible to realize a better target identification that is immune to the traditional ISAR jamming methods.…”