RCS of Complex Targets: Original Representation Validated by Measurements—Application to ISAR Imagery

Bennani, Yacine; Comblet, Fabrice; Khenchaf, Ali

doi:10.1109/tgrs.2012.2186972

Cited by 24 publications

(6 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…To calculate the RCS of the aircraft, we use the SBR method, PTD, and the flat model. The SBR method and PTD are well-known high-frequency techniques that can efficiently analyze the RCS of electrically large objects such as naval ships and aircrafts [33], [34]. The SBR method is based on the ray tracing technique and physical optics (PO), which can quantify the multiple reflections by determining the equivalent current densities induced on the surface of illuminated PEC planes, whereas PTD can calculate the diffraction fields based on the determination of the filamentary currents induced on illuminated of PEC edges.…”

Section: B Numerical Methods For Rcs Estimatementioning

confidence: 99%

Analysis of Radome Cross Section of an Aircraft Equipped With a FSS Radome

Shin

Yoon

et al. 2022

IEEE Access

View full text Add to dashboard Cite

We study the radar cross section (RCS) property of an aircraft considering the electromagnetic (EM) characteristics of a frequency selective surface (FSS) radome, mounted on the aircraft. Instead of full-wave methods, we utilize high-frequency methods, such as the shooting and bouncing rays (SBR), physical theory of diffraction (PTD), and flat model, taking a significant computational efficiency. Based on the developed algorithm, we analyze monostatic and bistatic RCSs of the aircraft equipped with either singleand multi-layer dielectric, FSS, or PEC radomes in terms of frequency and polarization. Our calculations are validated by comparing with those of the commercial EM simulator with significant computational efficiency. The present numerical study indicates that including the radome EM characteristics into the overall aircraft RCS study is crucial for the accurate RCS estimate INDEX TERMS Frequency selective surface (FSS), FSS radome, radar cross section (RCS), shooting and bouncing rays (SBR), physical theory of diffraction (PTD), flat model.

show abstract

Section: B Numerical Methods For Rcs Estimatementioning

confidence: 99%

Analysis of Radome Cross Section of an Aircraft Equipped With a FSS Radome

Shin

Yoon

et al. 2022

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Consequently, the total number of ISAR images in this database is 1944. For rigorous details about the experiments conducted on the anechoic chamber, the reader is refereed to [1,43]. Samples of each aircraft target class of this dataset are displayed in Figure 4.…”

Section: Database Descriptionmentioning

confidence: 99%

Radar Target Recognition Using Salient Keypoint Descriptors and Multitask Sparse Representation

et al. 2018

Self Cite

View full text Add to dashboard Cite

Abstract:In this paper, we propose a novel approach to recognize radar targets on inverse synthetic aperture radar (ISAR) and synthetic aperture radar (SAR) images. This approach is based on the multiple salient keypoint descriptors (MSKD) and multitask sparse representation based classification (MSRC). Thus, to characterize the targets in the radar images, we combine the scale-invariant feature transform (SIFT) and the saliency map. The purpose of this combination is to reduce the number of SIFT keypoints by keeping only those located in the target area (salient region); this speeds up the recognition process. After that, we compute the feature vectors of the resulting salient SIFT keypoints (MSKD). This methodology is applied for both training and test images. The MSKD of the training images leads to constructing the dictionary of a sparse convex optimization problem. To achieve the recognition, we adopt the MSRC taking into consideration each vector in the MSKD as a task. This classifier solves the sparse representation problem for each task over the dictionary and determines the class of the radar image according to all sparse reconstruction errors (residuals). The effectiveness of the proposed approach method has been demonstrated by a set of extensive empirical results on ISAR and SAR images databases. The results show the ability of the proposed method to predict adequately the aircraft and the ground targets.

show abstract

“…Consequently, the total number of ISAR images in this database is 1944. For a rigorous details about the experiments conducted on the anechoic chamber, the reader is refereed to [1,38]. Samples of each aircraft target class of this dataset are displayed in Figure 4.…”

Section: Database Descriptionmentioning

confidence: 99%

Radar Target Recognition using Salient Keypoint Descriptors and Multitask Sparse Representation

Karine¹,

Toumi²,

Khenchaf³

2018

Preprint

Self Cite

View full text Add to dashboard Cite

Abstract:In this paper, we propose a novel approach to recognize radar targets on inverse synthetic aperture radar (ISAR) and synthetic aperture radar (SAR) images. This approach is based on the multiple salient keypoint descriptors (MSKD) and multitask sparse representation based classification (MSRC). Thus, to characterize the targets in the radar images, we combine the scale-invariant feature transform (SIFT) and the saliency map. The goal of this combination is to reduce the SIFT keypoints and their time computing time by maintaining only those located in the target area (salient region). Then, we compute the feature vectors of the resulting salient SIFT keypoints (MSKD). This methodology is applied for both training and test images. The MSKD of the training images leads to construct the dictionary of a sparse convex optimization problem. To achieve the recognition, we adopt the MSRC taking into consideration each vector in the MSKD as a task. This classifier solves the sparse representation problem for each task over the dictionary and determines the class of the radar image according to all sparse reconstruction errors (residuals). The effectiveness of the proposed approach method has been demonstrated by a set of extensive empirical results on ISAR and SAR images databases. The results show the ability of our method to predict adequately the aircraft and the ground targets.

show abstract

RCS of Complex Targets: Original Representation Validated by Measurements—Application to ISAR Imagery

Cited by 24 publications

References 18 publications

Analysis of Radome Cross Section of an Aircraft Equipped With a FSS Radome

Analysis of Radome Cross Section of an Aircraft Equipped With a FSS Radome

Radar Target Recognition Using Salient Keypoint Descriptors and Multitask Sparse Representation

Radar Target Recognition using Salient Keypoint Descriptors and Multitask Sparse Representation

Contact Info

Product

Resources

About