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1
Matthews, Thomas J., François Rigal, Kostas A. Triantis, and Robert J. Whittaker. "A global model of island species–area relationships." Proceedings of the National Academy of Sciences 116, no. 25 (May 30, 2019): 12337–42. http://dx.doi.org/10.1073/pnas.1818190116.
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The increase in species richness with island area (ISAR) is a well-established global pattern, commonly described by the power model, the parameters of which are hypothesized to vary with system isolation and to be indicative of ecological process regimes. We tested a structural equation model of ISAR parameter variation as a function of taxon, isolation, and archipelago configuration, using a globally distributed dataset of 151 ISARs encompassing a range of taxa and archipelago types. The resulting models revealed a negative relationship between ISAR intercept and slope as a function of archipelago species richness, in turn shaped by taxon differences and by the amount and disposition of archipelago area. These results suggest that local-scale (intra-archipelago) processes have a substantial role in determining ISAR form, obscuring the diversity patterns predicted by island theory as a function of archipelago isolation. These findings have implications for the use and interpretation of ISARs as a tool within biogeography, ecology, and conservation.
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Sommers, Pacifica, Dorota L. Porazinska, John L. Darcy, Eli M. S. Gendron, Lara Vimercati, Adam J. Solon, and Steven K. Schmidt. "Microbial Species–Area Relationships in Antarctic Cryoconite Holes Depend on Productivity." Microorganisms 8, no. 11 (November 7, 2020): 1747. http://dx.doi.org/10.3390/microorganisms8111747.
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The island species–area relationship (ISAR) is a positive association between the number of species and the area of an isolated, island-like habitat. ISARs are ubiquitous across domains of life, yet the processes generating ISARs remain poorly understood, particularly for microbes. Larger and more productive islands are hypothesized to have more species because they support larger populations of each species and thus reduce the probability of stochastic extinctions in small population sizes. Here, we disentangled the effects of “island” size and productivity on the ISAR of Antarctic cryoconite holes. We compared the species richness of bacteria and microbial eukaryotes on two glaciers that differ in their productivity across varying hole sizes. We found that cryoconite holes on the more productive Canada Glacier gained more species with increasing hole area than holes on the less productive Taylor Glacier. Within each glacier, neither productivity nor community evenness explained additional variation in the ISAR. Our results are, therefore, consistent with productivity shaping microbial ISARs at broad scales. More comparisons of microbial ISARs across environments with limited confounding factors, such as cryoconite holes, and experimental manipulations within these systems will further contribute to our understanding of the processes shaping microbial biogeography.
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Schulz, Stefanie, Julinda Mehilli, Albert Schömig, and Adnan Kastrati. "ISAR." Circulation Journal 74, no. 9 (2010): 1771–78. http://dx.doi.org/10.1253/circj.cj-10-0738.
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Zhu, Xinli, Yasheng Zhang, Wang Lu, Yuqiang Fang, and Jun He. "An ISAR Image Component Recognition Method Based on Semantic Segmentation and Mask Matching." Sensors 23, no. 18 (September 18, 2023): 7955. http://dx.doi.org/10.3390/s23187955.
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The inverse synthetic aperture radar (ISAR) image is a kind of target feature data acquired by radar for moving targets, which can reflect the shape, structure, and motion information of the target, and has attracted a great deal of attention from the radar automatic target recognition (RATR) community. The identification of ISAR image components in radar satellite identification missions has not been carried out in related research, and the relevant segmentation methods of optical images applied to the research of semantic segmentation of ISAR images do not achieve ideal segmentation results. To address this problem, this paper proposes an ISAR image part recognition method based on semantic segmentation and mask matching. Furthermore, a reliable automatic ISAR image component labeling method is designed, and the satellite target component labeling ISAR image samples are obtained accurately and efficiently, and the satellite target component labeling ISAR image data set is obtained. On this basis, an ISAR image component recognition method based on semantic segmentation and mask matching is proposed in this paper. U-Net and Siamese Network are designed to complete the ISAR image binary semantic segmentation and binary mask matching, respectively. The component label of the ISAR image is predicted by the mask matching results. Experiments based on satellite component labeling ISAR image datasets confirm that the proposed method is feasible and effective, and it has greater comparative advantages compared to other classical semantic segmentation networks.
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Feng, Tiantian, and Lixin Guo. "Multiview ISAR Imaging for Complex Targets Based on Improved SBR Scattering Model." International Journal of Antennas and Propagation 2021 (January 25, 2021): 1–10. http://dx.doi.org/10.1155/2021/6615154.
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A novel multiview inverse synthetic aperture radar (ISAR) imaging method is proposed to simulate high-resolution and identifiable ISAR image of complex targets by handling large-angle and wide-bandwidth scattering data. The scattering data are simulated with the shooting and bouncing ray (SBR) method. The bidirectional ray-tracing algorithm is developed to reduce the computation time. Simulation results indicate that the improved method is efficient and reliable to calculate electromagnetic (EM) scattering of electrically large targets. To implement the multiview ISAR imaging method after data simulation, we divide the large-angle and wide-bandwidth scattering data into subaperture data and conduct ISAR image processing for each subaperture datum locally. Transforming each subaperture ISAR image to the global coordinate system and summing them together will produce the high-resolution ISAR image that is meaningful for the database set up for synthetic aperture radar automatic target recognition (SAR ATR). The final simulation ISAR images further validate the great performance of our scattering calculation algorithm and ISAR imaging method.
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Ni, Peishuang, Yanyang Liu, Hao Pei, Haoze Du, Haolin Li, and Gang Xu. "CLISAR-Net: A Deformation-Robust ISAR Image Classification Network Using Contrastive Learning." Remote Sensing 15, no. 1 (December 21, 2022): 33. http://dx.doi.org/10.3390/rs15010033.
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The inherent unknown deformations of inverse synthetic aperture radar (ISAR) images, such as translation, scaling, and rotation, pose great challenges to space target classification. To achieve high-precision classification for ISAR images, a deformation-robust ISAR image classification network using contrastive learning (CL), i.e., CLISAR-Net, is proposed for deformation ISAR image classification. Unlike traditional supervised learning methods, CLISAR-Net develops a new unsupervised pretraining phase, which means that the method uses a two-phase training strategy to achieve classification. In the unsupervised pretraining phase, combined with data augmentation, positive and negative sample pairs are constructed using unlabeled ISAR images, and then the encoder is trained to learn discriminative deep representations of deformation ISAR images by means of CL. In the fine-tuning phase, based on the deep representations obtained from pretraining, a classifier is fine-tuned using a small number of labeled ISAR images, and finally, the deformation ISAR image classification is realized. In the experimental analysis, CLISAR-Net achieves higher classification accuracy than supervised learning methods for unknown scaled, rotated, and combined deformations. It implies that CLISAR-Net learned more robust deep features of deformation ISAR images through CL, which ensures the performance of the subsequent classification.
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Deng, Jie, and Fulin Su. "SDRnet: A Deep Fusion Network for ISAR Ship Target Recognition Based on Feature Separation and Weighted Decision." Remote Sensing 16, no. 11 (May 27, 2024): 1920. http://dx.doi.org/10.3390/rs16111920.
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Existing methods for inverse synthetic aperture radar (ISAR) target recognition typically rely on a single high-resolution radar signal type, such as ISAR images or high-resolution range profiles (HRRPs). However, ISAR images and HRRP data offer representations of targets across different aspects, each containing valuable information crucial for radar target recognition. Moreover, the process of generating ISAR images inherently facilitates the acquisition of HRRP data, ensuring timely data collection. Therefore, to fully leverage the different information from both HRRP data and ISAR images and enhance ISAR ship target recognition performance, we propose a novel deep fusion network named the Separation-Decision Recognition network (SDRnet). First, our approach employs a convolutional neural network (CNN) to extract initial feature vectors from ISAR images and HRRP data. Subsequently, a feature separation module is employed to derive a more robust target representation. Finally, we introduce a weighted decision module to enhance overall predictive performance. We validate our method using simulated and measured data containing ten categories of ship targets. The experimental results confirm the effectiveness of our approach in improving ISAR ship target recognition.
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Luan, Jun, and Yun Neng Yuan. "Simulation and Effectiveness Analysis of Jamming on Inverse Synthetic Aperture Radar." Advanced Materials Research 765-767 (September 2013): 2762–65. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.2762.
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nverse synthetic aperture radar (ISAR) is a kind of imaging radar with high capability. It plays an important role in military applications, such as target classification, recognition, identification and accurate weapon navigation. This paper establishes a simulation model of jamming for ISAR system based on the principle of ISAR imaging. Then, the several typical of noise modulation jamming experiments for ISAR are carried out. The simulation results are analysed through evaluating the jamming effect based on the entropy concept.
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Zeng, Chuangzhan, Weigang Zhu, and Xin Jia. "Bistatic ISAR Sparse Imaging Method for High-Speed Moving Target Based on Dechirping Processing." International Journal of Antennas and Propagation 2019 (January 27, 2019): 1–15. http://dx.doi.org/10.1155/2019/9710968.
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Bistatic inverse synthetic aperture radar (ISAR) can increase the probability of tracking the high-speed target and provide more angle information than monostatic ISAR. However, bistatic ISAR suffers from a serious defocusing problem, resulting from the high-speed motion. Furthermore, the inherent geometry distortion and calibration problems make bistatic ISAR (B-ISAR) imaging more complex. In response to these problems, we propose a bistatic ISAR imaging method for high-speed moving target with geometric distortion correction and calibration based on dechirping processing. At first, based on the motion decomposition idea, the B-ISAR echo model of the high-speed moving target is established. Then, by analyzing the imaging mechanism of the Range-Doppler algorithm (RDA), we eliminate the phase items influencing the imaging quality with speed compensation and Doppler compensation. After that, the analytic formula of the geometric distortion factor and calibration factor are deduced, which helps transform the geometric correction and calibration problem into a parameter estimation problem. Finally, with the sparsity of the scattering points, the required parameters are solved using the expectation maximization (EM) algorithm based on the maximum a posteriori probability criterion. With the estimated parameters, a clear B-ISAR image of a high-speed moving target with geometric correction and calibration is obtained. The simulations show that the proposed method has a better resolution and simultaneously attains geometric correction and calibration of the image.
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Wang, Binbin, Hao Cha, Zibo Zhou, Huatao Tang, Lidong Sun, Baozhou Du, and Lei Zuo. "An Iterative Phase Autofocus Approach for ISAR Imaging of Maneuvering Targets." Electronics 10, no. 17 (August 30, 2021): 2100. http://dx.doi.org/10.3390/electronics10172100.
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Translational motion compensation and azimuth compression are two essential processes in inverse synthetic aperture radar (ISAR) imaging. The anterior process recovers coherence between pulses, during which the phase autofocus algorithm is usually used. For ISAR imaging of maneuvering targets, conventional phase autofocus methods cannot effectively eliminate the phase error due to the adverse influence of the quadratic phase terms caused by the target’s maneuvering motion, which leads to the blurring of ISAR images. To address this problem, an iterative phase autofocus approach for ISAR imaging of maneuvering targets is proposed in this paper. Considering the coupling between translational phase errors and quadratic phase terms, minimum entropy-based autofocus (MEA) method and adaptive modified Fourier transform (MFT) are performed iteratively to realize better imaging results. In this way, both the translational phase error and quadratic phase terms induced by target’s maneuvering motion can be compensated effectively, and the globally optimal ISAR image is obtained. Comparison ISAR imaging results indicates that the new approach achieves stable and better ISAR image under a simple procedure. Experimental results show that the image entropy of the proposed approach is 0.2 smaller than the MEA method, which validates the effectiveness of the new approach.
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Guo, Xinrong, Fengkai Liu, and Darong Huang. "Migration through Resolution Cell Correction and Sparse Aperture ISAR Imaging for Maneuvering Target Based on Whale Optimization Algorithm—Fast Iterative Shrinkage Thresholding Algorithm." Sensors 24, no. 7 (March 27, 2024): 2148. http://dx.doi.org/10.3390/s24072148.
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Targets faced by inverse synthetic aperture radar (ISAR) are often non-cooperative, with target maneuvering being the main manifestation of this non-cooperation. Maneuvers cause ISAR imaging results to be severely defocused, which can create huge difficulties in target identification. In addition, as the ISAR bandwidth continues to increase, the impact of migration through resolution cells (MTRC) on imaging results becomes more significant. Target non-cooperation may also result in sparse aperture, leading to the failure of traditional ISAR imaging algorithms. Therefore, this paper proposes an algorithm to realize MTRC correction and sparse aperture ISAR imaging for maneuvering targets simultaneously named whale optimization algorithm–fast iterative shrinkage thresholding algorithm (WOA-FISTA). In this algorithm, FISTA is used to perform MTRC correction and sparse aperture ISAR imaging efficiently and WOA is adopted to estimate the rotational parameter to eliminate the effects of maneuvering on imaging results. Experimental results based on simulation and measured datasets prove that the proposed algorithm implements sparse aperture ISAR imaging and MTRC correction for maneuvering targets simultaneously. The proposed algorithm achieves better results than traditional algorithms under different signal-to-noise ratio conditions.
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Bouzan, Jobar, Boris Stoilkov, Spyridon Nellas, and Marcus Horstmann. "Comparison of G8 and ISAR Screening Results in Geriatric Urology." Medicines 8, no. 8 (July 22, 2021): 40. http://dx.doi.org/10.3390/medicines8080040.
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Background: The G8 and ISAR scores are two different screening tools for geriatric risk factors and frailty. The aim of this study was to compare the G8 and ISAR screening results in a cohort of urogeriatric patients to help clinicians to better understand and choose between the two tests. Methods: We retrospectively evaluated 100 patients at the age of 75 and above who were treated for different urological conditions. All routinely and prospectively underwent G8 and ISAR screening tests. A G8 score ≤ 14 and an ISAR score > 2 were considered positive. The results for the two tests were compared and correlated to clinical data. Results: The mean age of the patients was 83 y (min. 75–max. 101); 78 of the patients were male, while 22 were female; 58 of the patients were G8-positive, while 42 were G8-negative; and 24 were ISAR-positive, while 76 ISAR were negative. All the ISAR-positive patients were also G8-positive. There was a significant negative correlation between the G8 and ISAR scores (r = −0.77, p < 0.001). Both tests correlated significantly with the Charlson comorbidity index, length of stay, number of coded diagnosis, and Braden score (p < 0.05). Conclusion: Both tests are significantly correlated with each other and to clinical data related to geriatric frailty. However, the G8 score has a much higher rate of positive tests, which limits its use in daily routine, and the ISAR score is therefore preferable. For “fit” geriatric patients, however, a negative G8 score can be of great use as a confirmatory test for further decision making.
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Mamatha, B., and V. Valli Kumar. "ISAR Image Classification with Wavelet and Watershed Transforms." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 6 (December 1, 2016): 3087. http://dx.doi.org/10.11591/ijece.v6i6.12116.
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<p>Inverse Synthetic Aperture Radar images are playing a significant role in classification of sea and air targets. First we acquire the ISAR images of targets using a sensor like radar and extract the characteristics of targets from the ISAR images in the form of feature vectors. The computed feature vectors are used for classification of targets. In this work, widely used and efficient segmentation tool Watershed transform and the multi resolution technique wavelet transform are explored to derive the target features. An artificial neural network based classifier is used for classification. The Wavelet analysis divides the information of an image into approximation and detail sub signals. The approximate and three detail sub signal values are taken as feature vectors and given as input to the classifier for ship ISAR image classification. The widely used segmentation technique, Watershed transform is applied to the ISAR images. The wavelet coefficients are computed for the segmented ISAR images and used as feature vectors for classification of the ISAR images. Also, the statistical moments mean and standard deviation are computed for the color ISAR images itself, taken in RGB format. These statistical color moments are used as feature vector. The classification accuracy is compared for the feature vectors.</p>
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Mamatha, B., and V. Valli Kumar. "ISAR Image Classification with Wavelet and Watershed Transforms." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 6 (December 1, 2016): 3087. http://dx.doi.org/10.11591/ijece.v6i6.pp3087-3093.
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<p>Inverse Synthetic Aperture Radar images are playing a significant role in classification of sea and air targets. First we acquire the ISAR images of targets using a sensor like radar and extract the characteristics of targets from the ISAR images in the form of feature vectors. The computed feature vectors are used for classification of targets. In this work, widely used and efficient segmentation tool Watershed transform and the multi resolution technique wavelet transform are explored to derive the target features. An artificial neural network based classifier is used for classification. The Wavelet analysis divides the information of an image into approximation and detail sub signals. The approximate and three detail sub signal values are taken as feature vectors and given as input to the classifier for ship ISAR image classification. The widely used segmentation technique, Watershed transform is applied to the ISAR images. The wavelet coefficients are computed for the segmented ISAR images and used as feature vectors for classification of the ISAR images. Also, the statistical moments mean and standard deviation are computed for the color ISAR images itself, taken in RGB format. These statistical color moments are used as feature vector. The classification accuracy is compared for the feature vectors.</p>
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Baraniak, Andrew P., Erika L. Lasda, Eric J. Wagner, and Mariano A. Garcia-Blanco. "A Stem Structure in Fibroblast Growth Factor Receptor 2 Transcripts Mediates Cell-Type-Specific Splicing by Approximating Intronic Control Elements." Molecular and Cellular Biology 23, no. 24 (December 15, 2003): 9327–37. http://dx.doi.org/10.1128/mcb.23.24.9327-9337.2003.
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ABSTRACT Alternative splicing of fibroblast growth factor receptor 2 (FGFR2) occurs in a cell-type-specific manner with the mutually exclusive use of exon IIIb or exon IIIc. Specific inclusion of exon IIIb is observed in epithelial cells, whereas exon IIIc inclusion is seen in mesenchymal cells. Epithelium-specific activation of exon IIIb and repression of exon IIIc are coordinately regulated by intronic activating sequence 2 (IAS2) and intronic splicing activator and repressor (ISAR) elements in FGFR2 pre-mRNA. Previously, it has been suggested that IAS2 and a 20-nucleotide core sequence of ISAR form a stem structure that allows for the proper regulation of FGFR2 alternative splicing. Replacement of IAS2 and the ISAR core with random sequences capable of stem formation resulted in the proper activation of exon IIIb and repression of exon IIIc in epithelial cells. Given the high degree of phylogenetic conservation of the IAS2-ISAR core structure and the fact that unrelated stem-forming sequences could functionally substitute for IAS2 and ISAR elements, we postulated that the stem structure facilitated the approximation of intronic control elements. Indeed, deletion of the entire stem-loop region and juxtaposition of sequences immediately upstream of IAS2 with sequences immediately downstream of the ISAR core maintained proper cell-type-specific inclusion of exon IIIb. These data demonstrate that IAS2 and the ISAR core are dispensable for the cell-type-specific activation of exon IIIb; thus, the major, if not the sole, role of the IAS2-ISAR stem in exon IIIb activation is to approximate sequences upstream of IAS2 with sequences downstream of the ISAR core. The downstream sequence is very likely a highly conserved GCAUG element, which we show was required for efficient exon IIIb activation.
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Ping, Cheng, and Zhao Jiaqun. "Fast off grid compressed sensing ISAR imaging algorithm." Journal of Electrical Engineering 69, no. 4 (August 1, 2018): 326–28. http://dx.doi.org/10.2478/jee-2018-0047.
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Abstract To solve the off grid problem in compressed sensing (CS) based inverse synthetic aperture radar (ISAR) imaging, a fast and accurate algorithm has been proposed in the paper. By jointly estimating the off grid error and the sparse solution, off grid ISAR imaging is transformed into a joint optimization problem. Interestingly, it can be solved efficiently through two least squares problems based on first order Taylor approximation. When applied to complex sinusoids and quasi real ISAR data, the proposed algorithm has got better results than the conventional algorithm. Therefore, it is a promising off grid CS based ISAR imaging algorithm.
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Yin, Zhiping, Xinfei Lu, and Weidong Chen. "Echo Preprocessing to Enhance SNR for 2D CS-Based ISAR Imaging Method." Sensors 18, no. 12 (December 13, 2018): 4409. http://dx.doi.org/10.3390/s18124409.
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A new CS-based inverse synthetic aperture radar (ISAR) imaging framework is proposed to enhance both the image performance and the robustness at a low SNR. An ISAR echo preprocessing method for enhancing the ISAR imaging quality of compressed sensing (CS) based algorithms is developed by implementing matched filtering, echo denoising and matrix optimization sequentially. After the preprocessing, the two-dimensional (2D) SL0 algorithm is applied to reconstruct an ISAR image in the range and cross-range plane through a series of 2D matrices using the 2D CS theory, rather than converting the 2D convex optimization problem to the one-dimensional (1D) problem in the image reconstruction process. The proposed preprocessing framework is verified by simulations and experiment. Simulations and experimental results show that the ISAR image obtained by the 2D sparse recovery algorithm with our proposed method has a better performance.
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Wiedemann, Andreas, Jens Püttmann, and Hans Heppner. "Der ISAR-positive Patient in der Urologie." Aktuelle Urologie 50, no. 01 (December 13, 2018): 100–105. http://dx.doi.org/10.1055/a-0736-3722.
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Zusammenfassung Hintergrund Seit 2015 ist mit dem ISAR-Screening (Identification of Seniors at Risk) wie in vielen Bundesländern ein geriatrisches Eingangsscreening bei allen über 75-jährigen Krankenhauspatienten vorgeschrieben. Unklar ist bisher, wie die als „mit geriatrischem Handlungsbedarf“ identifizierten Patienten in der Urologie weiter charakterisiert sind und wie die hier enthaltenen Informationen in den klinischen Alltag einfließen könnten. Methoden Vom 01.07.2016 bis 31.12.2016 wurden 337 Patienten identifiziert, die über 75-jährig dem ISAR-Screening unterzogen werden sollten. Das Ergebnis („mit“ oder „ohne“ geriatrischem Handlungsbedarf) wurde den Ergebnissen weitergehender Assessments (Sturzrisiko, Dekubitusrisiko, Mangelernährungsscreening), demografischen Daten (Alter, Aufnahmestatus) und urologischen Diagnosen gegenübergestellt. Ergebnisse Von 377 im Untersuchungszeitraum aufgenommenen Patienten wurden 102 „ISAR-positiv“ getestet. Diese Patienten waren signifikant älter als „ISAR-negative“ Patienten, sie wiesen ein signifikant höheres Sturz-, Dekubitus- und Mangelernährungsrisiko auf und gelangten häufiger per Notfalleinweisung in das Krankenhaus. ISAR-positive Patienten wiesen als Ausdruck der Multimorbidität signifikant mehr Diagnosen im DRG-Satz auf – es dominierten urologische Malignome und entzündliche Erkrankungen des Harntraktes. Während prozesskonform nahezu alle definitionsgemäß zu untersuchenden Patienten dem ISAR-Screening unterzogen wurden, war der Durchdringungsgrad der nachfolgenden Assessments geringer. Schlussfolgerung Das ISAR-Screening beschreibt sehr gut schon im Rahmen der Aufnahmesituation den vulnerablen, multimorbiden und von Chronifizierung und Autonomieverlust bedrohten „geriatrischen“ Patienten in der Urologie. Die Herausforderung der Zukunft wird sein, die hier gewonnenen Informationen in die klinische urologische Routine zu implementieren.
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Wang, Hetong, Qi Yang, Hongqiang Wang, and Bin Deng. "Autofocusing of Maneuvering Targets in Terahertz Inverse Synthetic Aperture Radar Imaging Based on Damped Newton Method." Sensors 22, no. 18 (September 12, 2022): 6883. http://dx.doi.org/10.3390/s22186883.
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Maneuvering target imaging based on inverse synthetic aperture radar (ISAR) imaging has recently drawn significant attention. Among the many autofocusing technologies which are crucial in ISAR imaging, minimum-entropy-based autofocusing (MEA) is highly robust. However, traditional MEA is not suitable for terahertz (THz) ISAR imaging. For one thing, the iterative process in traditional MEA is too complicated to be utilized for THz-ISAR imaging with tremendous data. For another, THz wavelengths are very short and extremely sensitive to phase errors, so the compensation accuracy of the traditional MEA method can hardly meet the requirements of THz radar high-resolution imaging. Therefore, in this paper, the MEA algorithm based on the damped Newton method is proposed, which improves computational efficiency by approximating the first- and second-order partial derivatives of the image entropy function with respect to the phase errors, as well as by the fast Fourier transform (FFT). The search step size factor is introduced to ensure that the algorithm can converge along the declination direction of the entropy function and obtain the globally optimal ISAR image. The experimental results validated the efficiency of the proposed algorithm, which is promising in THz-ISAR imaging of maneuvering targets.
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Li, Wenzhe, Yanxin Yuan, Yuanpeng Zhang, and Ying Luo. "Unblurring ISAR Imaging for Maneuvering Target Based on UFGAN." Remote Sensing 14, no. 20 (October 21, 2022): 5270. http://dx.doi.org/10.3390/rs14205270.
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Inverse synthetic aperture radar (ISAR) imaging for maneuvering targets suffers from a Doppler frequency time-varying problem, leading to the ISAR images blurred in the azimuth direction. Given that the traditional imaging methods have poor imaging performance or low efficiency, and the existing deep learning imaging methods cannot effectively reconstruct the deblurred ISAR images retaining rich details and textures, an unblurring ISAR imaging method based on an advanced Transformer structure for maneuvering targets is proposed. We first present a pseudo-measured data generation method based on the DeepLabv3+ network and Diamond-Square algorithm to acquire an ISAR dataset for training with good generalization to measured data. Next, with the locally-enhanced window Transformer block adopted to enhance the ability to capture local context as well as global dependencies, we construct a novel Uformer-based GAN (UFGAN) to restore the deblurred ISAR images with rich details and textures from blurred imaging results. The simulation and measured experiments show that the proposed method can achieve fast and high-quality imaging for maneuvering targets under the condition of a low signal-to-noise ratio (SNR) and sparse aperture.
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Shi, Lin, Xiaoxiu Zhu, Chaoxuan Shang, Baofeng Guo, Juntao Ma, and Ning Han. "High-Resolution Bistatic ISAR Imaging of a Space Target with Sparse Aperture." Electronics 8, no. 8 (August 7, 2019): 874. http://dx.doi.org/10.3390/electronics8080874.
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Due to the large size of space targets, migration through resolution cells (MTRC) are induced by a rotational motion in high-resolution bistatic inverse synthetic aperture radar (Bi-ISAR) systems. The inaccurate correction of MTRC degrades the quality of Bi-ISAR images. However, it is challenging to correct the MTRC where sparse aperture data exists for Bi-ISAR systems. A joint approach of MTRC correction and sparse high-resolution imaging for Bi-ISAR systems is presented in this paper. First, a Bi-ISAR imaging sparse model-related to MTRC is established based on compress sensing (CS). Second, the target image elements and noise are modeled as the complex Laplace prior, and the Gaussian prior, respectively. Finally, the high-resolution, well-focused image is obtained by the full Bayesian inference method, without manual adjustments of unknown parameters. Simulated results verify the effectiveness and robustness of the proposed algorithm.
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Martorella, M., A. Cacciamano, E. Giusti, F. Berizzi, B. Haywood, and B. Bates. "CLEAN Technique for Polarimetric ISAR." International Journal of Navigation and Observation 2008 (August 12, 2008): 1–12. http://dx.doi.org/10.1155/2008/325279.
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Inverse synthetic aperture radar (ISAR) images are often used for classifying and recognising targets. To reduce the amount of data processed by the classifier, scattering centres are extracted from the ISAR image and used for classifying and recognising targets. This paper addresses the problem of estimating the position and the scattering vector of target scattering centres from polarimetric ISAR images. The proposed technique is obtained by extending the CLEAN technique, which was introduced in radar imaging for extracting scattering centres from single-polarisation ISAR images. The effectiveness of the proposed algorithm, namely, the Polarimetric CLEAN (Pol-CLEAN) is tested on simulated and real data.
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Yan, Zhishuo, Yi Zhang, and Heng Zhang. "A Hybrid SAR/ISAR Approach for Refocusing Maritime Moving Targets with the GF-3 SAR Satellite." Sensors 20, no. 7 (April 4, 2020): 2037. http://dx.doi.org/10.3390/s20072037.
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Due to self-motion and sea waves, moving ships are typically defocused in synthetic aperture radar (SAR) images. To focus non-cooperative targets, the inverse SAR (ISAR) technique is commonly used with motion compensation. The hybrid SAR/ISAR approach allows a long coherent processing interval (CPI), in which SAR targets are processed with ISAR processing, and exploits the advantages of both SAR and ISAR to generate well-focused images of moving targets. In this paper, based on hybrid SAR/ISAR processing, we propose an improved rank-one phase estimation method (IROPE). By using an iterative two-step convergence approach in the IROPE, the proposed method achieves accurate phase error, maintains robustness to noise and performs well in estimating various phase errors. The performance of the proposed method is analyzed by comparing it with other focusing algorithms in terms of processing simulated data and real complex image data acquired by Gaofen-3 (GF-3) in spotlight mode. The results demonstrate the effectiveness of the proposed method.
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Li, Chenchen J., and Hao Ling. "Wide-Angle, Ultra-Wideband ISAR Imaging of Vehicles and Drones." Sensors 18, no. 10 (October 2, 2018): 3311. http://dx.doi.org/10.3390/s18103311.
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In-situ, wide-angle, and ultra-wideband inverse synthetic aperture radar (ISAR) imaging of vehicles and drones is demonstrated using a portable ultra-wideband radar. In order to form well-focused ISAR images, motion compensation is performed before applying the k-space imaging algorithm. While the same basic motion compensation methodology is applied to both types of targets, a more complex motion model is needed to better capture the flight path of the drone. The resulting ISAR images clearly show the geometrical outline of the targets and highlight locations of prominent backscattering. The ISAR images are also assessed against images generated through instrumented targets or laboratory measurements, and the image quality is shown to be comparable.
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Shi, Hongyin, Saixue Xia, Qi Qin, Ting Yang, and Zhijun Qiao. "Non-Stationary Platform Inverse Synthetic Aperture Radar Maneuvering Target Imaging Based on Phase Retrieval." Sensors 18, no. 10 (October 5, 2018): 3333. http://dx.doi.org/10.3390/s18103333.
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As a powerful signal processing tool for imaging moving targets, placing radar on a non-stationary platform (such as an aerostat) is a future direction of Inverse Synthetic Aperture Radar (ISAR) systems. However, more phase errors are introduced into the received signal due to the instability of the radar platform, making it difficult for popular algorithms to accurately perform motion compensation, which leads to severe effects in the resultant ISAR images. Moreover, maneuvering targets may have complex motion whose motion parameters are unknown to radar systems. To overcome the issue of non-stationary platform ISAR autofocus imaging, a high-resolution imaging method based on the phase retrieval principle is proposed in this paper. Firstly, based on the spatial geometric and echo models of the ISAR maneuvering target, we can deduce that the radial motion of the radar platform or the vibration does not affect the modulus of the ISAR echo signal, which provides a theoretical basis for the phase recovery theory for the ISAR imaging. Then, we propose an oversampling smoothness (OSS) phase retrieval algorithm with prior information, namely, the phase of the blurred image obtained by the classical imaging algorithm replaces the initial random phase in the original OSS algorithm. In addition, the size of the support domain of the OSS algorithm is set with respect to the blurred target image. Experimental simulation shows that compared with classical imaging methods, the proposed method can obtain the resultant motion-compensated ISAR image without estimating the radar platform and maneuvering target motion parameters, wherein the fictitious target is perfectly focused.
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Donlon, C., I. S. Robinson, W. Wimmer, G. Fisher, M. Reynolds, R. Edwards, and T. J. Nightingale. "An Infrared Sea Surface Temperature Autonomous Radiometer (ISAR) for Deployment aboard Volunteer Observing Ships (VOS)." Journal of Atmospheric and Oceanic Technology 25, no. 1 (January 1, 2008): 93–113. http://dx.doi.org/10.1175/2007jtecho505.1.
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Abstract The infrared SST autonomous radiometer (ISAR) is a self-calibrating instrument capable of measuring in situ sea surface skin temperature (SSTskin) to an accuracy of 0.1 K. Extensive field deployments alongside two independent research radiometers measuring SSTskin using different spectral and geometric configurations show that, relatively, ISAR SSTskin has a zero bias ±0.14 K rms. The ISAR instrument has been developed for satellite SST validation and other scientific programs. The ISAR can be deployed continuously on voluntary observing ships (VOS) without any service requirement or operator intervention for periods of up to 3 months. Five ISAR instruments have been built and are in sustained use in the United States, China, and Europe. This paper describes the ISAR instrument including the special design features that enabled a single channel radiometer with a spectral bandpass of 9.6–11.5 μm to be adapted for autonomous use. The entire instrument infrared optical path is calibrated by viewing two blackbody reference cavities at different temperatures to maintain high accuracy while tolerating moderate contamination of optical components by salt deposition. During bad weather, an innovative storm shutter, triggered by a sensitive optical rain gauge, automatically seals the instrument from the external environment. Data are presented that verify the instrument calibration and functionality in such situations. A watchdog timer and auto-reboot function support automatic data logging recovery in case of power outages typically encountered on ships. An RS485 external port allows supporting instruments that are not part of the core ISAR package (e.g., a solarimeter) to be logged using the ISAR system. All data are processed by the ISAR instrument and are relayed to a host computer via the RS232 serial link as (National Electronics Manufacturers Association) NEMA-style strings allowing easy integration into many commercial onboard scientific data logging systems. In case of a communications failure, data are stored on board using a CompactFlash card that can be retrieved when the instrument is serviced. The success of the design is demonstrated using results obtained over 21 months in the English Channel and Bay of Biscay as part of a campaign to validate SSTskin observations derived from the Environmental Satellite (Envisat) Advanced Along-Track Scanning Radiometer (AATSR).
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Hou, Chongyuan, Rongzhi Zhang, Kaizhong Yang, Xiaoyong Li, Yang Yang, Xin Ma, Gang Guo, Yuan Yang, Lei Liu, and Feng Zhou. "Non-Cooperative Target Attitude Estimation Method Based on Deep Learning of Ground and Space Access Scene Radar Images." Mathematics 11, no. 3 (February 2, 2023): 745. http://dx.doi.org/10.3390/math11030745.
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Determining the attitude of a non-cooperative target in space is an important frontier issue in the aerospace field, and has important application value in the fields of malfunctioning satellite state assessment and non-cooperative target detection in space. This paper proposes a non-cooperative target attitude estimation method based on the deep learning of ground and space access (GSA) scene radar images to solve this problem. In GSA scenes, the observed target satellite can be imaged not only by inverse synthetic-aperture radar (ISAR), but also by space-based optical satellites, with space-based optical images providing more accurate attitude estimates for the target. The spatial orientation of the intersection of the orbital planes of the target and observation satellites can be changed by fine tuning the orbit of the observation satellite. The intersection of the orbital planes is controlled to ensure that it is collinear with the position vector of the target satellite when it is accessible to the radar. Thus, a series of GSA scenes are generated. In these GSA scenes, the high-precision attitude values of the target satellite can be estimated from the space-based optical images obtained by the observation satellite. Thus, the corresponding relationship between a series of ISAR images and the attitude estimation of the target at this moment can be obtained. Because the target attitude can be accurately estimated from the GSA scenes obtained by a space-based optical telescope, these attitude estimation values can be used as training datasets of ISAR images, and deep learning training can be performed on ISAR images of GSA scenes. This paper proposes an instantaneous attitude estimation method based on a deep network, which can achieve robust attitude estimation under different signal-to-noise ratio conditions. First, ISAR observation and imaging models were created, and the theoretical projection relationship from the three-dimensional point cloud to the ISAR imaging plane was constructed based on the radar line of sight. Under the premise that the ISAR imaging plane was fixed, the ISAR imaging results, theoretical projection map, and target attitude were in a one-to-one correspondence, which meant that the mapping relationship could be learned using a deep network. Specifically, in order to suppress noise interference, a UNet++ network with strong feature extraction ability was used to learn the mapping relationship between the ISAR imaging results and the theoretical projection map to achieve ISAR image enhancement. The shifted window (swin) transformer was then used to learn the mapping relationship between the enhanced ISAR images and target attitude to achieve instantaneous attitude estimation. Finally, the effectiveness of the proposed method was verified using electromagnetic simulation data, and it was found that the average attitude estimation error of the proposed method was less than 1°.
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Zhu, Xiaoxiu, Limin Liu, Baofeng Guo, Wenhua Hu, Lin Shi, and Juntao Ma. "Two-Dimensional ISAR Fusion Imaging of Block Structure Targets." International Journal of Antennas and Propagation 2021 (August 2, 2021): 1–18. http://dx.doi.org/10.1155/2021/6613975.
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The range resolution and azimuth resolution are restricted by the limited transmitting bandwidth and observation angle in a monostatic radar system. To improve the two-dimensional resolution of inverse synthetic aperture radar (ISAR) imaging, a fast linearized Bregman iteration for unconstrained block sparsity (FLBIUB) algorithm is proposed to achieve multiradar ISAR fusion imaging of block structure targets. First, the ISAR imaging echo data of block structure targets is established based on the geometrical theory of the diffraction model. The multiradar ISAR fusion imaging is transformed into a signal sparse representation problem by vectorization operation. Then, considering the block sparsity of the echo data of block structure targets, the FLBIUB algorithm is utilized to achieve the block sparse signal reconstruction and obtain the fusion image. The algorithm further accelerates the iterative convergence speed and improves the imaging efficiency by combining the weighted back-adding residual and condition number optimization of the basis matrix. Finally, simulation experiments show that the proposed method can effectively achieve block sparse signal reconstruction and two-dimensional multiradar ISAR fusion imaging of block structure targets.
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Wimmer, Werenfrid, and Ian S. Robinson. "The ISAR Instrument Uncertainty Model." Journal of Atmospheric and Oceanic Technology 33, no. 11 (November 2016): 2415–33. http://dx.doi.org/10.1175/jtech-d-16-0096.1.
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AbstractMeasurements of sea surface temperature at the skin interface () made by an Infrared Sea Surface Temperature Autonomous Radiometer (ISAR) have been used for a number of years to validate satellite sea surface temperature (SST), especially high-accuracy observations such as made by the Advanced Along-Track Scanning Radiometer (AATSR). The ISAR instrument accuracy for measuring is ±0.1 K (Donlon et al.), but to satisfy Quality Assurance Framework for Earth Observation (QA4EO) principles and metrological standards (Joint Committee for Guides in Metrology), an uncertainty model is required. To develop the ISAR uncertainty model, all sources of uncertainty in the instrument are analyzed and an uncertainty value is assigned to each component. Finally, the individual uncertainty components are propagated through the ISAR retrieval algorithm to estimate a total uncertainty for each measurement. The resulting ISAR uncertainty model applied to a 12-yr archive of measurements from the Bay of Biscay shows that 77.6% of the data are expected to be within ±0.1 K and a further 17.2% are within 0.2 K.
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Rui, Cui, Ji Fei Pan, Li Min Gi Chen, and Jing Zhu. "A New Evaluation Method of Jamming Effect on ISAR." Applied Mechanics and Materials 380-384 (August 2013): 4132–35. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.4132.
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The Inverse Synthetic Aperture Radar (ISAR) is a high resolution imaging radar. Some methods used to evaluate the jamming effect of general radar are unsuitable to ISAR. A new evaluation method of jamming effect on ISAR is presented in the paper through comparing the change of moment character and area character, The amplitude modulation (AM) noise jamming and frequency modulation (FM) noise jamming are carried out in the paper, the results of simulation prove the method is corrective and effective.
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Wu, Linhua, Lizhi Zhao, Junling Wang, Jiaoyang Su, and Weijun Cheng. "ISAR Image Registration Based on Line Features." Journal of Electromagnetic Engineering and Science 24, no. 3 (May 31, 2024): 215–25. http://dx.doi.org/10.26866/jees.2024.3.r.222.
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Inverse synthetic aperture radar (ISAR) image registration enables the analysis of target dynamics by comparing registered images from different viewpoints. However, it faces significant challenges due to various factors, such as the complex scattering characteristics of the target, limited availability of information, and additive noise in ISAR images. This paper proposes a novel ISAR image registration method based on line features. It integrates information from both dominant scatterers and the target’s outer contour to detect lines. According to the consistency principles of multiple lines in rotation and translation, line features from different ISAR images are matched. Simultaneously, the results of the feature matching are utilized to guide the parameter configuration for optimizing the image registration process. Comparative experiments illustrate the advantages of the proposed method in both feature extraction and registration feasibility.
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Martorella, M., J. Palmer, F. Berizzi, B. Haywood, and B. Bates. "Polarimetric ISAR autofocusing." IET Signal Processing 2, no. 3 (2008): 312. http://dx.doi.org/10.1049/iet-spr:20070121.
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Ping, Cheng, and Zhao Jiaqun. "New Compressed Sensing ISAR Imaging Algorithm Based on Log–Sum Minimization." Journal of Electrical Engineering 67, no. 6 (December 1, 2016): 439–43. http://dx.doi.org/10.1515/jee-2016-0064.
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Abstract To improve the performance of inverse synthetic aperture radar (ISAR) imaging based on compressed sensing (CS), a new algorithm based on log-sum minimization is proposed. A new interpretation of the algorithm is also provided. Compared with the conventional algorithm, the new algorithm can recover signals based on fewer measurements, in looser sparsity condition, with smaller recovery error, and it has obtained better sinusoidal signal spectrum and imaging result for real ISAR data. Therefore, the proposed algorithm is a promising imaging algorithm in CS ISAR.
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Zhang, Liqi, and Yanlei Li. "An Image Registration Method Based on Correlation Matching of Dominant Scatters for Distributed Array ISAR." Sensors 22, no. 4 (February 21, 2022): 1681. http://dx.doi.org/10.3390/s22041681.
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Distributed array radar provides new prospects for three-dimensional (3D) inverse synthetic aperture radar (ISAR) imaging. The accuracy of image registration, as an essential part of 3D ISAR imaging, affects the performance of 3D reconstruction. In this paper, the imaging process of distributed array ISAR is proposed according to the imaging model. The ISAR images of distributed array radar at different APCs have different distribution of scatters. When the local distribution of scatters for the same target are quite different, the performance of the existing ISAR image registration methods may not be optimal. Therefore, an image registration method is proposed by integrating the feature-based method and the area-based method. The proposed method consists of two stages: coarse registration and fine registration. In the first stage, a dominant scatters model is established based on scale-invariant feature transform (SIFT). In the second stage, sub-pixel precision registration is achieved using the local correlation matching method. The effectiveness of the proposed method is verified by comparison with other image registration methods. The 3D reconstruction of the registered experimental data is carried out to assess the practicability of the proposed method.
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Chen, Jiyuan, Xiaoyi Pan, Letao Xu, and Wei Wang. "Bistatic ISAR Imaging with a V-FM Waveform Based on a Dual-Channel-Coupled 2D-CS Algorithm." Sensors 18, no. 9 (September 13, 2018): 3082. http://dx.doi.org/10.3390/s18093082.
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Due to the sparsity of the space distribution of point scatterers and radar echo data, the theory of Compressed Sensing (CS) has been successfully applied in Inverse Synthetic Aperture Radar (ISAR) imaging, which can recover an unknown sparse signal from a limited number of measurements by solving a sparsity-constrained optimization problem. In this paper, since the V style modulation(V-FM) signal can mitigate the ambiguity apparent in range and velocity, the dual-channel, two-dimension, compressed-sensing (2D-CS) algorithm is proposed for Bistatic ISAR (Bi-ISAR) imaging, which directly deals with the 2D signal model for image reconstruction based on solving a nonconvex optimization problem. The coupled 2D super-resolution model of the target’s echoes is firstly established; then, the 2D-SL0 algorithm is applied in each channel with different dictionaries, and the final image is obtained by synthesizing the two channels. Experiments are used to test the robustness of the Bi-ISAR imaging framework with the two-dimensional CS method. The results show that the framework is capable accurately reconstructing the Bi-ISAR image within the conditions of low SNR and low measured data.
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Brink, Mark, Thu Lan Nguyen, Dirk Schreckenberg, and Jördis Wothge. "Introducing ICBEN's new socio-acoustic survey archive ISAR." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 268, no. 7 (November 30, 2023): 1216–19. http://dx.doi.org/10.3397/in_2023_0184.
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The International Committee on Biological Effects of Noise (ICBEN) has recently started an international archive of original survey response data from so called socio-acoustic noise surveys, named ISAR (ICBEN Socio-Acoustic Survey Archive). The ISAR archive caters to noise effects researchers around the globe and enables pooled analyses, establishment of generalized exposure-response relationships, trend analyses both spatially and temporally, as well as cross-cultural comparisons of self-reported noise reactions (noise annoyance and noise-induced sleep disturbances in particular). The data in the archive will be freely accessible. Data in the ISAR archive are fully anonymous in order to meet the pertinent data protection regulations in most countries. The ISAR archive is curated by ICBENs officers and the team chairs of Team 6 (Community response to noise and annoyance). The ISAR archive explicitly collects only raw survey response data, rather than data that have been aggregated on a higher level. We are constantly seeking to extend the archive with new study data and thus, every new contribution is highly welcome. In this talk, we would like to publicize and promote the archive and encourage our colleagues to share their survey data with the archive.
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Wang, Yiding, Yuanhao Li, Jiongda Song, and Guanghui Zhao. "Random Stepped Frequency ISAR 2D Joint Imaging and Autofocusing by Using 2D-AFCIFSBL." Remote Sensing 16, no. 14 (July 9, 2024): 2521. http://dx.doi.org/10.3390/rs16142521.
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With the increasingly complex electromagnetic environment faced by radar, random stepped frequency (RSF) has garnered widespread attention owing to its remarkable Electronic Counter-Countermeasure (ECCM) characteristic, and it has been universally applied in inverse synthetic aperture radar (ISAR) in recent years. However, if the phase error induced by the translational motion of the target in RSF ISAR is not precisely compensated, the imaging result will be defocused. To address this challenge, a novel 2D method based on sparse Bayesian learning, denoted as 2D-autofocusing complex-value inverse-free SBL (2D-AFCIFSBL), is proposed to accomplish joint ISAR imaging and autofocusing for RSF ISAR. First of all, to integrate autofocusing into the ISAR imaging process, phase error estimation is incorporated into the imaging model. Then, we increase the speed of Bayesian inference by relaxing the evidence lower bound (ELBO) to avoid matrix inversion, and we further convert the iterative process into a matrix form to improve the computational efficiency. Finally, the 2D phase error is estimated through maximum likelihood estimation (MLE) in the image reconstruction iteration. Experimental results on both simulated and measured datasets have substantiated the effectiveness and computational efficiency of the proposed 2D joint imaging and autofocusing method.
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Lei, Xue, Cui Rui, and Zhu Jing. "Research on Jamming Effect Evaluation Method of ISAR Using Moment Invariants." Applied Mechanics and Materials 40-41 (November 2010): 909–12. http://dx.doi.org/10.4028/www.scientific.net/amm.40-41.909.
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The Inverse Synthetic Aperture Radar (ISAR) is a high resolution imaging radar. Some methods used to evaluate the jamming effect of general radar are unsuitable to ISAR. Considering the principle of radar jamming and the theory of image disposal, the paper gives a new evaluation method of barrage jamming effect on ISAR, it evaluates the jamming effect through calculating the difference of two picture’s Moment Invariants, and this method can reflect distortion degree of the jammed target during the whole jamming process. The results of simulation prove the method is corrective and effective.
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Graf, CE, SV Giannelli, T. Chevalley, FR Herrmann, FP Sarasin, and JP Michel. "Identification of older patients at risk of unplanned readmission after discharge from the emergency department." Swiss Medical Weekly 143, no. 0102 (January 1, 2012): w13327. http://dx.doi.org/10.57187/smw.2012.13327.
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STUDY HYPOTHESIS: The Identification of Senior At Risk (ISAR) and the Triage Risk Stratification Tool (TRST) are the two most studied screening tools to detect high-risk patients for unplanned readmission after an emergency department (ED)-visit. Since their performance was unclear among ED-patients over 75 years, we evaluated their capacities to predict readmission at 1, 3, 6 and 12 months as well as their usefulness in avoiding unnecessary further comprehensive geriatric assessment (CGA) in negative screened patients. METHODS: Historical cohort study with systematic routine data collection of functional status, comorbid conditions and readmission rate of patients released home after an ED-visit between 2007 and 2009 at the Geneva University Hospitals. RESULTS: 345 patients were included (mean age 84y; 63% female). Readmission rates were 25%, 38%, 49%, and 60% at 1, 3, 6, and 12 months, respectively. Positive ISAR (≥2/6) and TRST (≥2/5) predicted modestly unplanned readmission at each time point (AUC range: 0.607–0.664). Prediction of readmission with ISAR or TRST was not modified after adjustment for variables significantly associated with readmission (being male, having poor functional or comorbid scores). In case of negative ISAR or TRST, their high negative predictive values (NPV) would safely allow avoiding 64 useless CGA (ISAR <2: 7/64 readmissions at 1 month). CONCLUSIONS: Both ISAR and TRST tools predicted modestly unplanned readmission after an ED-visit among patients over 75 years. Nevertheless, due to their low specificity and high NPV these screening tools are useful to select elderly ED-patients who can safely return home without any further CGA.
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Wei, Shunjun, Jiadian Liang, Mou Wang, Xiangfeng Zeng, Jun Shi, and Xiaoling Zhang. "CIST: An Improved ISAR Imaging Method Using Convolution Neural Network." Remote Sensing 12, no. 16 (August 16, 2020): 2641. http://dx.doi.org/10.3390/rs12162641.
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Compressive sensing (CS) has been widely utilized in inverse synthetic aperture radar (ISAR) imaging, since ISAR measured data are generally non-completed in cross-range direction, and CS-based imaging methods can obtain high-quality imaging results using under-sampled data. However, the traditional CS-based methods need to pre-define parameters and sparse transforms, which are tough to be hand-crafted. Besides, these methods usually require heavy computational cost with large matrices operation. In this paper, inspired by the adaptive parameter learning and rapidly reconstruction of convolution neural network (CNN), a novel imaging method, called convolution iterative shrinkage-thresholding (CIST) network, is proposed for ISAR efficient sparse imaging. CIST is capable of learning optimal parameters and sparse transforms throughout the CNN training process, instead of being manually defined. Specifically, CIST replaces the linear sparse transform with non-linear convolution operations. This new transform and essential parameters are learnable end-to-end across the iterations, which increases the flexibility and robustness of CIST. When compared with the traditional state-of-the-art CS imaging methods, both simulation and experimental results demonstrate that the proposed CIST-based ISAR imaging method can obtain imaging results of high quality, while maintaining high computational efficiency. CIST-based ISAR imaging is tens of times faster than other methods.
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Xie, Yaocong, Xiaoping Li, Fangfang Shen, Zheng Mao, Bowen Bai, and Xuyang Chen. "Influence of Plasma Sheath’s Velocity Field on ISAR Imaging of Hypersonic Target." Remote Sensing 14, no. 15 (August 6, 2022): 3799. http://dx.doi.org/10.3390/rs14153799.
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Plasma sheath poses a serious challenge to inverse synthetic aperture radar (ISAR) imaging of hypersonic targets. This paper investigated the distribution characteristics of the electron density and velocity field in the plasma sheath surrounding the hypersonic target in various flight scenes. The incident depth and reflective surface of electromagnetic (EM) waves with X-band, Ku-band, and Ka-band can be determined based on the plasma frequency. We established the echo model coupled with the velocity field of the plasma sheath on the reflective surface and obtained one-dimensional range profiles and ISAR images of the hypersonic target in various flight scenes. The simulation results indicated that the non-uniform velocity field on the reflective surface induced displacement and diffusion in the one-dimensional range profile, resulting in ISAR image distortion. A changing flight scene and radar frequency can have an impact on imaging results. Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) were utilized to assess the impact of plasma sheath on ISAR images. This study revealed the defocus mechanism of the ISAR image caused by the velocity field of the plasma sheath and provided a theoretical reference for the selection of radar frequency for hypersonic targets in various flight scenes.
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Wang, Jiadong, Yachao Li, Ming Song, Pingping Huang, and Mengdao Xing. "Noise Robust High-Speed Motion Compensation for ISAR Imaging Based on Parametric Minimum Entropy Optimization." Remote Sensing 14, no. 9 (May 1, 2022): 2178. http://dx.doi.org/10.3390/rs14092178.
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When a target is moving at high-speed, its high-resolution range profile (HRRP) will be stretched by the high-order phase error caused by the high velocity. In this case, the inverse synthetic aperture radar (ISAR) image would be seriously blurred. To obtain a well-focused ISAR image, the phase error induced by target velocity should be compensated. This article exploits the variation continuity of a high-speed moving target’s velocity and proposes a noise-robust high-speed motion compensation algorithm for ISAR imaging. The target’s velocity within a coherent processing interval (CPI) is modeled as a high-order polynomial based on which a parametric high-speed motion compensation signal model is developed. The entropy of the ISAR image after high-speed motion compensation is treated as an evaluation metric, and a parametric minimum entropy optimization model is established to estimate the velocity and compensate it simultaneously. A gradient-based solver of this optimization is then adopted to iteratively find the optimal solution. Finally, the high-order phase error caused by the target’s high-speed motion can be iteratively compensated, and a well-focused ISAR image can be obtained. Extensive simulation experiments have verified the noise robustness and effectiveness of the proposed algorithm.
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Xv, Jia-Hua, Xiao-Kuan Zhang, Bin-feng Zong, and Shu-Yu Zheng. "A Side-Lobe Denoise Process for ISAR Imaging Applications: Combined Fast Clean and Spatial Focus Technique." Remote Sensing 16, no. 13 (June 21, 2024): 2279. http://dx.doi.org/10.3390/rs16132279.
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The presence of side-lobe noise degrades the image quality and adversely affects the performance of inverse synthetic aperture radar (ISAR) image understanding applications, such as automatic target recognition (ATR), target detection, etc. However, methods reliant on data processing, such as windowing, inevitably encounter resolution reduction, and current deep learning approaches under-appreciate the sparsity inherent in ISAR images. Taking the above analysis into consideration, a convolutional neural network-based process for ISAR side-lobe noise training is proposed in this paper. The proposed processing, based on the ISAR images sparsity characteristic analysis, undergoes enhancements in three core ideas, dataset construction, prior network design, and loss function improvements. In the realm of dataset construction, we introduce a bin-by-bin fast clean algorithm and accelerate the processing speed significantly on the basis of image complete information. Subsequently, a spatial attention layer is incorporated into the prior network designed to augment the network’s focus on the crucial regions of ISAR images. In addition, a loss function featuring a weighting factor is devised to ensure the precise recovery of the strong scattering point. Simulation experiments demonstrate that the proposed process achieves significant improvements in both quantitative and qualitative results over the classical denoise methods.
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Feng, Junjie, Yinan Sun, and XiuXia Ji. "High-Resolution ISAR Imaging Based on Improved Sparse Signal Recovery Algorithm." Wireless Communications and Mobile Computing 2021 (April 2, 2021): 1–7. http://dx.doi.org/10.1155/2021/5541116.
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In order to solve the problem of high-resolution ISAR imaging under the condition of finite pulses, an improved smoothed L0 norm (SL0) sparse signal reconstruction ISAR imaging algorithm is proposed. Firstly, the ISAR imaging is transformed into the optimization problem of minimum L0 norm. Secondly, a single-loop structure is used instead of two loop layers in SL0 algorithm which increases the searching density of variable parameter to ensure the recovery accuracy. Finally, the compared step is added to ensure the optimization solution along the steepest descent gradient direction. The experimental results show that the proposed algorithm has better imaging effect.
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Yang, Hong, Yasheng Zhang, and Wenzhe Ding. "A Fast Recognition Method for Space Targets in ISAR Images Based on Local and Global Structural Fusion Features with Lower Dimensions." International Journal of Aerospace Engineering 2020 (February 14, 2020): 1–21. http://dx.doi.org/10.1155/2020/3412582.
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Feature extraction is the key step of Inverse Synthetic Aperture Radar (ISAR) image recognition. However, limited by the cost and conditions of ISAR image acquisition, it is relatively difficult to obtain large-scale sample data, which makes it difficult to obtain target deep features with good discriminability by using the currently popular deep learning method. In this paper, a new method for low-dimensional, strongly robust, and fast space target ISAR image recognition based on local and global structural feature fusion is proposed. This method performs the trace transformation along the longest axis of the ISAR image to generate the global trace feature of the space target ISAR image. By introducing the local structural feature, Local Binary Pattern (LBP), the complementary fusion of the global and local features is achieved, which makes up for the missing structural information of the trace feature and ensures the integrity of the ISAR image feature information. The representation of trace and LBP features in a low-dimensional mapping feature space is found by using the manifold learning method. Under the condition of maintaining the local neighborhood relationship in the original feature space, the effective fusion of trace and LBP features is achieved. So, in the practical application process, the target recognition accuracy is no longer affected by trace function, LBP feature block number selection, and other factors, realizing the high robustness of the algorithm. To verify the effectiveness of the proposed algorithm, an ISAR image database containing 1325 samples of 5 types of space targets is used for experiments. The results show that the classification accuracy of the 5 types of space targets can reach more than 99%, and the recognition accuracy is no longer affected by the trace feature and LBP feature selection, which has strong robustness. The proposed method provides a fast and effective high-precision model for space target feature extraction, which can give some references for solving the problem of space object efficient identification under the condition of small sample data.
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Lu, Liangyou, Peng Chen, and Lenan Wu. "A RPCA-Based ISAR Imaging Method for Micromotion Targets." Sensors 20, no. 10 (May 25, 2020): 2989. http://dx.doi.org/10.3390/s20102989.
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Micro-Doppler generated by the micromotion of a target contaminates the inverse synthetic aperture radar (ISAR) image heavily. To acquire a clear ISAR image, removing the Micro-Doppler is an indispensable task. By exploiting the sparsity of the ISAR image and the low-rank of Micro-Doppler signal in the Range-Doppler (RD) domain, a novel Micro-Doppler removal method based on the robust principal component analysis (RPCA) framework is proposed. We formulate the model of sparse ISAR imaging for micromotion target in the framework of RPCA. Then, the imaging problem is decomposed into iterations between the sub-problem of sparse imaging and Micro-Doppler extraction. The alternative direction method of multipliers (ADMM) approach is utilized to seek for the solution of each sub-problem. Furthermore, to improve the computational efficiency and numerical robustness in the Micro-Doppler extraction, an SVD-free method is presented to further lessen the calculative burden. Experimental results with simulated data validate the effectiveness of the proposed method.
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Li, Yuanyuan, Yaowen Fu, and Wenpeng Zhang. "High-resolution Distributed ISAR Imaging based on Sparse Representation." MATEC Web of Conferences 214 (2018): 02004. http://dx.doi.org/10.1051/matecconf/201821402004.
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Distributed ISAR technique has the potential to increase the cross-range resolution by exploiting multi-channel echoes from distributed virtual equivalent sensors. In the existing imaging approaches, the echoes acquired from different sensors are rearranged into an equivalent single-channel ISAR signal. Then, the missing data between the observation angles of any two adjacent sensors is restored by interpolation. However, the interpolation method can be very inaccurate when the gap is large or the signal-to-noise (SNR) of echoes is low. In this paper, we discuss sparse representation of distributed ISAR echoes since the scattering field of the target is usually composed of only a limited number of strong scattering centres, representing strong spatial sparsity. Then, by using sparse algorithm (Orthogonal Matching Pursuit algorithm, OMP), the positions and amplitudes of the scattering points in every range bin can be recovered and the final ISAR image with high cross-range resolution can be obtained. Results show the effectiveness of the proposed method.
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Zhou, Zibo, Libing Jiang, and Zhuang Wang. "A novel image registration method for InISAR 3D imaging." MATEC Web of Conferences 232 (2018): 02044. http://dx.doi.org/10.1051/matecconf/201823202044.
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Image registration is a key intermediate step for Interferometric Inverse Synthetic Aperture Radar (InISAR) three-dimensional (3D) imaging. It arranges the same scatterers of the target on the same pixel cell in different ISAR images, which makes the interferometric processing carried on between the same scatterers to obtain its 3D coordinates. This paper proposes a novel ISAR image registration method of three steps. Firstly, chirp Fourier transform is used to estimate the rotational angular velocity of the target. Secondly, the compensation phase is constructed, according to the rotational angular velocity, to eliminate the wave path difference between different radars echoes. Finally, two-dimensional (2D) Fourier transform is used to yield registered ISAR images. The proposed method achieves the ISAR image registration through phase compensation in echo field, therefore, no extra computation is needed in image field. The experiment results demonstrate the advantages of the proposed method in precision, computation efficiency and practicability.
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Zeng, Cao, Mengyi Qin, Dong Li, Hongqing Liu, and Yi Chai. "An Efficient ISAR Imaging of Targets with Complex Motions Based on a Quasi-Time-Frequency Analysis Bilinear Coherent Algorithm." Sensors 18, no. 9 (August 26, 2018): 2814. http://dx.doi.org/10.3390/s18092814.
Full textAbstract:
The inverse synthetic aperture radar (ISAR) imaging for targets with complex motions has always been a challenging task due to the time-varying Doppler parameter, especially at the low signal-to-noise ratio (SNR) condition. In this paper, an efficient ISAR imaging algorithm for maneuvering targets based on a noise-resistance bilinear coherent integration is developed without the parameter estimation. First, the received signals of the ISAR in a range bin are modelled as a multicomponent quadratic frequency-modulated (QFM) signal after the translational motion compensation. Second, a novel quasi-time-frequency representation noise-resistance bilinear Radon-cubic phase function (CPF)-Fourier transform (RCFT) is proposed, which is based on the coherent integration of the energy of auto-terms along the slope line trajectory. In doing so, the RCFT also effectively suppresses the cross-terms and spurious peaks interference at no expense of the time-frequency resolution loss. Third, the cross-range positions of target’s scatters in ISAR image are obtained via a simple maximization projection from the RCFT result to the Doppler centroid axis, and the final high-resolution ISAR image is thus produced by regrouping all the range-Doppler frequency centroids. Compared with the existing time-frequency analysis-based and parameter estimation-based ISAR imaging algorithms, the proposed method presents the following features: (1) Better cross-term interference suppression at no time-frequency resolution loss; (2) computationally efficient without estimating the parameters of each scatters; (3) higher signal processing gain because of 2-D coherent integration realization and its bilinear function feature. The simulation results are provided to demonstrate the performance of the proposed method.
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Li, Xiuhe, Jinhe Ran, Yanbo Wen, Shunjun Wei, and Wei Yang. "MVFRnet: A Novel High-Accuracy Network for ISAR Air-Target Recognition via Multi-View Fusion." Remote Sensing 15, no. 12 (June 10, 2023): 3052. http://dx.doi.org/10.3390/rs15123052.
Full textAbstract:
Inverse Synthetic Aperture Radar (ISAR) is a promising technique for air target imaging and recognition. However, the traditional monostatic ISAR only can provide partial features of the observed target, which is a challenge for high-accuracy recognition. In this paper, to improve the recognition accuracy of air targets, we propose a novel recognition network based on multi-view ISAR imaging and fusion, called Multi-View Fusion Recognition network (MVFRnet). The main structure of MVFRnet consists of two components, the image fusion module and the target recognition module. The fusion module is used for multi-view ISAR data and image preprocessing and mainly performs imaging spatial match, image registration, and weighted fusion. The recognition network consists of the Skip Connect Unit and the Gated Channel Transformation (GCT) attention module, where the Skip Connect Unit ensures the extraction of global depth features of the image and the attention module enhances the perception of shallow contour features of the image. In addition, MVFRnet has a strong perception of image details and suppresses the effect of noise. Finally, simulated and real data are used to verify the effectiveness of the proposed scheme. Multi-view ISAR echoes of six types of aircraft are produced by electromagnetic simulation software. In addition, we also build a millimeter wave ground-based bistatic ISAR experiment system and collect multi-view data from an aircraft model. The simulation and experiment results demonstrate that the proposed scheme can obtain a higher recognition accuracy compared to other state-of-the-art methods. The recognition accuracy can be improved by approximately 30% compared with traditional monostatic recognition.