Scattering Center Research Articles

Two-Dimensional Scattering Center Estimation for Radar Target Recognition Based on Multiple High-Resolution Range Profiles.

A new estimation strategy on locations of two-dimensional target scattering centers for radar target recognition is developed by using multiple high-resolution range profiles (HRRPs). Based on the range information contained in multiple HRRPs obtained from various observation angles, the estimated target scattering centers can be successfully located at the intersection points of the lines passing through the multiple HRRP points. This geometry-based algorithm can significantly reduce the computational complexity while ensuring the ability to estimate the two-dimensional target scattering centers. The computational complexity is formulated and compared to that of the conventional methods based on the synthetic aperture radar (SAR) images and HRRP sequences. In order to verify the performance of the proposed algorithm, the numerical and experimental results for three different types of aircraft were compared to those from SAR images. At the end of this article, the estimated radar scattering centers are used as the target features for the conventional classifier machine to confirm its target classification performance.

Radar Target Radar Cross-Section Measurement Based on Enhanced Imaging and Scattering Center Extraction.

Accurate measurement of a Radar Cross-Section (RCS) is a critical technical challenge in assessing the stealth performance and scattering characteristics of radar targets. Traditional RCS measurement methods are limited by high costs, sensitivity to environmental conditions, and difficulties in distinguishing local scattering features of targets. To address these challenges, this paper proposes a novel RCS measurement method based on enhanced imaging and scattering center extraction. This method integrates sub-aperture imaging with image fusion techniques to improve imaging quality and enhance the detail of target scattering characteristics. Additionally, an improved sequence CLEAN algorithm is employed to effectively suppress sidelobe effects and ensure the accuracy of scattering center extraction. Experimental results demonstrate that this method achieves higher precision in RCS measurement of complex targets and is particularly effective in environments with strong interference, where it successfully separates the scattering contributions of the target from those of the interference sources. This method offers a new technological approach for precise RCS measurement of radar stealth targets in the future.

Intelligent RCS Extrapolation Technology of Target Inspired by Physical Mechanism Based on Scattering Center Model

In this paper, a technology named SCM−ANN combining physical scattering mechanisms and artificial intelligence is proposed to realize radar cross-section (RCS) extrapolation of non-cooperative conductor targets with higher efficiency. Firstly, an adaptive scattering center (SC) extraction algorithm is used to construct the scattering center model (SCM) for non-cooperative targets from radar echoes in the low-frequency band (LFB). Secondly, an artificial neural network (ANN) is constructed to capture the nonlinear relationship between the real LFB echoes and those reconstructed from the SCM. Finally, the SCM is used to reconstruct echoes in the high-frequency band (HFB), and these reconstructions, together with the trained ANN, optimize the extrapolated HFB RCS. For the SCM−ANN technology, physical mechanistic modes are used for trend prediction, and artificial intelligence is used for regression optimization based on trend prediction. Simulation results show that the proposed method can achieve a 50% frequency extrapolation range, with an average prediction error reduction of up to 40% compared with the traditional scheme. By incorporating physical mechanisms, this proposed approach offers improved accuracy and an extended extrapolation range compared with the RCS extrapolation techniques relying solely on numerical prediction.

An Efficient Scattering Center Extraction Method Based on Stationary Phase Method With Quadratic Patch Models

An Efficient Scattering Center Extraction Method Based on Stationary Phase Method With Quadratic Patch Models

Low Scattering Performance and Scattering Center Characteristics of Streamlined Space Target

Low Scattering Performance and Scattering Center Characteristics of Streamlined Space Target

Forward Modeling of Robust Scattering Centers from Dynamic Ships on Time-Varying Sea Surfaces for Remote Sensing Target-Recognition Applications

This paper presents a forward modeling method for the scattering center (SC) model of dynamic ships on time-varying sea surfaces, tailored for remote sensing and target-recognition applications. Grounded in ship hydrodynamics, the methodology delineates ship movements amidst fluctuating waves, harnessing computer graphics to integrate ship–sea geometries across diverse temporal instances. Utilizing the four-path model, the composite scattering effects are segregated into distinct ship and sea contributions, along with their mutual interactions. Augmented by high-frequency electromagnetic principles, the paper quantifies and deduces SC parameters, culminating in a 3-D parameterized SC model for complex maritime targets. Unlike conventional inverse methods, this approach employs a “cause-to-effect” forward strategy, establishing clear links between SCs and local geometries, enhancing the model’s physical clarity. Using the fishing ship as a case, this research compared the normalized similarity index and position-matching rate between the reconstructed synthetic aperture radar (SAR) image and the simulated SAR image. The results indicate that all computed results exceeded 90%. Furthermore, a comparison was conducted between the reconstructed radar cross-sections (RCS) obtained by expanding the model within a large angular range and the simulated results. The root mean square error between the two was less than 3 dB, affirming the accuracy and effectiveness of the proposed model. Additionally, the research examines the variations in SCs during the six-degrees-of-freedom motions, providing a detailed quantitative analysis of their temporal trends in amplitude and position. In summary, this investigation furnishes an efficient and economical framework for rapid radar characterization in dynamic, variable marine environments, fostering advancements in remote sensing and maritime target identification.

Forward Establishment of Bistatic Component-Level Attributed Scattering Center Model for Complex Targets

Forward Establishment of Bistatic Component-Level Attributed Scattering Center Model for Complex Targets

EM Scattering Center Model-Guided Passive SAR Deception Using Diverse Frequency Time-Modulation

EM Scattering Center Model-Guided Passive SAR Deception Using Diverse Frequency Time-Modulation

An Efficient Attributed Scattering Center Extraction Method Accelerated by Dictionary Reuse

An Efficient Attributed Scattering Center Extraction Method Accelerated by Dictionary Reuse

3-D Attributed Scattering Center Model of Dynamic Group Targets by Forward Parametric Modeling Method

3-D Attributed Scattering Center Model of Dynamic Group Targets by Forward Parametric Modeling Method

Study the Impact of Silica Nanoparticles on the Properties of Several Dyes for the Fabrication of a Random Laser Gain Medium

Random laser gain media is synthesized with different types of dye at the same concentration (1×10-3 M) as an active material and silicon dioxide NPs (silica SiO2) as scatter centers through the Sol-Gel technique. The prepared samples are tested with UV–Vis spectroscopy, Fluorescence Spectroscopy, Field Emission Scanning Electron Microscopy (FESEM), and Energy Dispersive X-ray Diffraction (EDX). The end result demonstrates that doped dyes with silica nanoparticles at a concentration of 0.0016 mol/ml have lower absorbance and higher fluorescence spectra than pure dyes. FESEM scans revealed that the morphology of nanocrystalline silica is clusters of nano-sized spherical particles in the range (25-67) nm. It is concluded that the various dyes with SiO2 as a scattering center can be proposed to build laser media.

A Robust Scattering Center Extraction Method and Its Application in InISAR Imaging

A Robust Scattering Center Extraction Method and Its Application in InISAR Imaging

Sliding Scattering Center Extraction for Streamlined Radar Targets

Modern man-made targets with high-speed movement capabilities are often designed as special streamlined structures. As a result, when illuminated by radar, the surface of such targets will produce a sliding scattering center whose position changes with the radar line of sight. In this paper, we present a feature extraction technique for this kind of scattering centers. The trajectory of the equivalent scattering points in the one-dimensional (1-D) high-resolution range profile (HRRP) sinogram is first predicted by utilizing the special geometric characteristics of sliding scatterers. The parameters of sliding scattering features are then extracted by combining the basis pursuit algorithm with the predicted trajectory. Examples from both numerical and measured data are presented to demonstrate the usefulness of the proposed technique.

A Novel Multiband Fusion Method Based on Differential Processing of Scattering Centers to Eliminate Incoherence Between Sub-Bands

In traditional multiband fusion (MF) methods, the differences in the scattering center (SC) among different sub-bands are ignored, which leads to incoherence between sub-band echoes and results in limited estimation accuracy for full-band echo (FBE). To solve this problem, a novel MF method based on differential processing of the SCs in each sub-band is proposed and experimentally demonstrated. In this method, the SCs in each sub-band are classified using a proposed two-layer contrast strategy into two kinds of SCs, which are fused and super-resolved, respectively, to produce the FBE. For application during the classification, fusion, and super-resolution processes, a general iterative shrinkage thresholding algorithm is developed to ensure both the accuracy and speed of estimation of the SC locations and amplitudes. Total FBE is calculated by superimposing the fused FBE and the super-resolved FBE. In experiments, the bandwidth of each sub-band is improved by an average factor of 3.97 when the signal-to-noise ratio is equal to 10 dB and the frequency gap between two sub-bands is equal to 5.08 GHz. Thus, the high estimation accuracy of the proposed method is verified, as well as its high robustness to noise and incoherence.

Modified ADMM-Net for Attributed Scattering Center Decomposition of Synthetic Aperture Radar Targets

Modified ADMM-Net for Attributed Scattering Center Decomposition of Synthetic Aperture Radar Targets

SCMA: A Scattering Center Model Attack on CNN-SAR Target Recognition

Convolutional neural networks (CNNs) have been widely used in SAR (Synthetic Aperture Radar) target recognition, which can extract feature automatically. However, due to its own structural flaws, CNNs are easy to be fooled by adversarial examples, even if they have excellent performance. In this letter, a novel attack named scattering center model attack (SCMA) is designed, and its generation process does not rely on the prior knowledge of any neural network. Therefore, we can get a stable way which can be applied to any neural network. In addition, an improved scattering center model extraction method, which is the pre-part of SCMA, can filter out the useless noise to optimize the stability of attack. In the experiment, SCMA is compared with advanced attack algorithms. From the experimental results, it is clear to find that SCMA has excellent performance in terms of transfer attack success rate. Furthermore, visualization and interpretability analysis underpin the theoretical feasibility of SCMA.

Attributed Scattering Center Guided Adversarial Attack for DCNN SAR Target Recognition

Recently, deep learning has made significant progress in synthetic aperture radar automatic target recognition (SAR ATR). However, deep convolutional neural networks (DCNNs) are discovered to be susceptible to carefully crafted adversarial perturbations. Regarding the unique scattering mechanism in SAR imaging, the scattering feature such as attributed scattering centers (ASCs) should be deeply considered in the adversarial attack (AA) algorithms for DCNNs in SAR ATR. In this letter, an AA algorithm named ASC-STA is proposed to take advantage of the powerful SAR imaging property characterization capability of ASC model. Considering the imaging characteristic which is presented in the ASC model, the spatial transformation module is specially designed to be performed on the strong back-scattering structures guided by the reconstruction image of ASC model. Spatial transformation shifts the robust scattering point features via altering the locally gray-scale relationships of the target microstructures in SAR images. Besides, a modified shape context evaluated metric is established to assess the validity of the feature alternation process. Experimental results on the MSTAR database demonstrate that the proposed method achieves a high success rate without complex parameter settings and generates the perturbed image in high image quality. Compared with the norm-based AA algorithms, which are not involved with ASCs, the point feature of the original image is efficiently altered. At the same time, the perturbations are focused on the target regions accurately.

Global Scattering Center Representation of Target Wide-Angle Single Reflection/Diffraction Mechanisms Based on the Multiple Manifold Concept

Many radar applications, such as system design and testing and target detection and recognition, need to compress and rapidly reconstruct the target-scattering characteristics data through some suitable sparse representations, while the scattering center (SC) model resulting from different scattering mechanisms is just a prospective candidate. For the target scattering characteristics data with single reflection/diffraction mechanisms, the multimanifold structures of wide-angle SCs were revealed in light of asymptotic high-frequency theory and ray theory of electromagnetic field. Then the multimanifold clustering and curve/surface fitting algorithms are introduced to construct the target global SC (GSC) model. Compared with simulation data of sphere-cone target, the RCS at elevation 90°, azimuth 0–180° can be accurately reconstructed by only 77 parameters, the compress rate and root mean square error are 0.66 and 1.17 dB respectively. Simulation results showed that the GSC model could greatly compress the wide-angle scattering data while ensuring a suitable reconstruction accuracy. The proposed multimanifold GSC representation is convenient to implement and can effectively replace the redundant original scattering characteristics data.

Efficient Scattering Center Prediction Method for Targets With Coating Defects Through Deep Learning

The dielectric coating defects existing on the surface of low detectable targets have nonnegligible impacts on their electromagnetic (EM) scattering characteristics. Due to the large electrical dimension of the target, traditional EM analysis methods are inefficient and cannot meet the requirement of real-time analysis. To address this issue, this article proposed a U-net-based deep neural network (DNN) to perform efficient scattering center (SC) prediction for targets with coating defects from the input 2-D geometric image of it. Furthermore, facing the difficulty of limited training dataset due to the high cost of full-wave numerical simulations, this article proposed a transfer learning method by pretraining the network on a large dataset obtained by the shooting and bouncing ray (SBR) and then fine-tuning it on the target small dataset obtained by the multilevel fast multipole method (MLFMM), which greatly reduced the training difficulty and improved the accuracy and generalization ability of the model. Numerical results are presented to evaluate the performance of the proposed method. It is proven that the proposed method is promising in providing efficient SC prediction in real-time EM analysis scenarios.

False Scattering Center Extraction Based on Template Matching Method

When electromagnetic (EM) coupling exists, false peaks occur periodically in high-resolution range profile (HRRP). These false peaks restrict the improvement of the success rate of the target recognition based on HRRPs. Therefore, identifying these false peaks and removing them is crucial, but has not been thoroughly investigated in the open literature. This letter first studies the EM coupling mechanism in a multiobject system according to the iterative physical optics theory. Then, templates to match the false peaks are constructed based on the established EM coupling theory. Next, cross correlation is performed to recognize and remove the false peaks. To verify the presented method, a metallic multitriangle model is used. The simulation results show that the proposed method can remove the false peaks and find the accurate locations of the metallic triangles. Finally, the practicability of the method is proved through experimental measurement results.