Global Scattering Center Model Extraction of Radar Targets Based on Wideband Measurements

Zhou, Jianxiong; Zhao, Hongzhong; Shi, Zhongke; Fu, Qiang

doi:10.1109/tap.2008.924698

Cited by 56 publications

(8 citation statements)

References 19 publications

(38 reference statements)

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“…At present, the difficulties of RATR still mainly lie in the aspects of radar data preprocessing, target feature extraction performance, classifier design, etc. [57].…”

Section: Deficiencies and Challenges Of Traditional Ratr Methodsmentioning

confidence: 99%

Radar Target Characterization and Deep Learning in Radar Automatic Target Recognition: A Review

Jiang

Wang

et al. 2023

Remote Sensing

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Radar automatic target recognition (RATR) technology is fundamental but complicated system engineering that combines sensor, target, environment, and signal processing technology, etc. It plays a significant role in improving the level and capabilities of military and civilian automation. Although RATR has been successfully applied in some aspects, the complete theoretical system has not been established. At present, deep learning algorithms have received a lot of attention and have emerged as potential and feasible solutions in RATR. This paper mainly reviews related articles published between 2010 and 2022, which corresponds to the period when deep learning methods were introduced into RATR research. In this paper, the current research status of radar target characteristics is summarized, including motion, micro-motion, one-dimensional, and two-dimensional characteristics, etc. This paper reviews the progress of deep learning methods in the feature extraction and recognition of radar target characteristics in recent years, including space, air, ground, sea-surface targets, etc. Due to more and more attention and research results published in the past few years, it is hoped that this review can provide potential guidance for future research and application of deep learning in fields related to RATR.

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“…At present, the difficulties of RATR still mainly lie in the aspects of radar data preprocessing, target feature extraction performance, classifier design, etc. [57].…”

Section: Deficiencies and Challenges Of Traditional Ratr Methodsmentioning

confidence: 99%

Radar Target Characterization and Deep Learning in Radar Automatic Target Recognition: A Review

Jiang

Wang

et al. 2023

Remote Sensing

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“…In this section, we focus on the multi-station 3-D reconstruction geometry and signal model. According to [30], the spatial target scattering centers can be divided into two types: the first type of scattering centers is fixed on the target body, and does not change with the radar LOS. These scattering centers are called stable scattering centers and include the telemetry window, concavity, and missile tail.…”

Section: Multi-station 3-d Reconstruction Geometry and Signal Modelmentioning

confidence: 99%

Three-Dimensional Absolute Attitude Reconstruction of a Rigid Body Based on Multi-Station HRRP Sequences

Dai

Liu

et al. 2020

IEEE Access

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This study proposes a three-dimensional (3-D) absolute attitude reconstruction algorithm of a rigid body target based on multi-station high-resolution range profile (HRRP) sequences. The existing 3-D reconstruction algorithms rely on the target motion form and the reconstructed attitude is inaccurate due to the mismatch of the micro-motion model. In this study, the coordinates of the scattering centers are first calculated from the HRRP sequences of each radar using the factorization method. Then, the multistation data are associated with the reconstructed scattering center coordinates, and the absolute coordinates is estimated by the relationship between the associated radar line-of-sights (LOSs) and the absolute LOSs. The 3-D reconstruction algorithms for the stable scattering centers and the sliding scattering centers are studied. Numerical simulations verify that the proposed algorithm can reconstruct the 3-D absolute attitude of a rigid body target and is not affected by the mismatch of the target micro-motion model. INDEX TERMSAbsolute attitude, reconstruction before association, factorization method, singular value decomposition (SVD) method.

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“…where the parameters for DSC-D and DSC-R in (11) and (13) (A 1 , A 4 , L 1 , L 4 )can be calculated directly by the geometric parameters of the wing; the parameters for LSCs, A i , γ i and φ i , need to be estimated. The TFR obtained by PMLFMA and the one simulated by the PTD-based model are presented in Fig.…”

Section: Target B: Wing Of An Uvamentioning

confidence: 99%

“…At high frequencies, the scattering response of an object can be described as the sum of responses from individual scattering centers [1]. Many scattering center models [2][3][4][5][6][7] are exploited to provide parametric descriptions of scattering characteristics of targets, and they are widely applied in the area of radar, such as radar image interpretation, automatic target recognition and geometry reconstruction [8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

High Accuracy Scattering Center Modeling Based on PO and PTD

Zhao¹,

Guo²,

Sheng³

2018

RADIOENGINEERING

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Based on the solutions of physical optics (PO) and physical optics of diffraction (PTD), we propose a modified parametric scattering center model for high precise signal simulations of extended targets. Different from geometrical theory of diffraction (GTD), which is commonly used in the existing models, the solution of PTD represents the individual scattering contribution of edge diffraction, and can precisely describe the scattering field under full observation angles and polarizations. This model has higher precision than the existing ones, especially for the targets with dominant scattering centers induced by diffraction. And this model is physical where the parameters are related with geometry of the targets. To validate this model, four conducting targets with dominant contributions of diffraction are simulated in this study. The radar cross-sections (RCS) and the time-frequency representations (TFR) of backscattered waves simulated by this model are compared with those computed by a full-wave numerical method, as well as those simulated by a commonly used scattering model. The comparisons show that the results of this model have better agreement with those of the full-wave numerical method than the existing model.

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Global Scattering Center Model Extraction of Radar Targets Based on Wideband Measurements

Cited by 56 publications

References 19 publications

Radar Target Characterization and Deep Learning in Radar Automatic Target Recognition: A Review

Radar Target Characterization and Deep Learning in Radar Automatic Target Recognition: A Review

Three-Dimensional Absolute Attitude Reconstruction of a Rigid Body Based on Multi-Station HRRP Sequences

High Accuracy Scattering Center Modeling Based on PO and PTD

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