To realize effective co-phasing adjustment in large-aperture sparse-aperture telescopes,a multichannel stripe tracking approach is employed,allowing simultaneous interferometric measurements of multiple optical paths ...To realize effective co-phasing adjustment in large-aperture sparse-aperture telescopes,a multichannel stripe tracking approach is employed,allowing simultaneous interferometric measurements of multiple optical paths and circumventing the need for pairwise measurements along the mirror boundaries in traditional interferometric methods.This approach enhances detection efficiency and reduces system complexity.Here,the principles of the multibeam interference process and construction of a co-phasing detection module based on direct optical fiber connections were analyzed using wavefront optics theory.Error analysis was conducted on the system surface obtained through multipath interference.Potential applications of the interferometric method were explored.Finally,the principle was verified by experiment,an interferometric fringe contrast better than 0.4 is achieved through flat field calibration and incoherent digital synthesis.The dynamic range of the measurement exceeds 10 times of the center wavelength of the working band(1550 nm).Moreover,a resolution better than one-tenth of the working center wavelength(1550 nm)was achieved.Simultaneous three-beam interference can be achieved,leading to a 50%improvement in detection efficiency.This method can effectively enhance the efficiency of sparse aperture telescope co-phasing,meeting the requirements for observations of 8-10 m telescopes.This study provides a technological foundation for observing distant and faint celestial objects.展开更多
This paper concentrates on super-resolution imaging of the ship target under the sparse aperture situation.Firstly,a multi-static configuration is utilized to solve the coherent processing interval(CPI)problem caused ...This paper concentrates on super-resolution imaging of the ship target under the sparse aperture situation.Firstly,a multi-static configuration is utilized to solve the coherent processing interval(CPI)problem caused by the slow-speed motion of ship targets.Then,we realize signal restoration and image reconstruction with the alternating direction method of multipliers(ADMM).Furthermore,we adopt the interferometric technique to produce the three-dimensional(3D)images of ship targets,namely interferometric inverse synthetic aperture radar(InISAR)imaging.Experiments based on the simulated data are utilized to verify the validity of the proposed method.展开更多
In recent years,deeps learning has been widely applied in synthetic aperture radar(SAR)image processing.However,the collection of large-scale labeled SAR images is challenging and costly,and the classification accurac...In recent years,deeps learning has been widely applied in synthetic aperture radar(SAR)image processing.However,the collection of large-scale labeled SAR images is challenging and costly,and the classification accuracy is often poor when only limited SAR images are available.To address this issue,we propose a novel framework for sparse SAR target classification under few-shot cases,termed the transfer learning-based interpretable lightweight convolutional neural network(TL-IL-CNN).Additionally,we employ enhanced gradient-weighted class activation mapping(Grad-CAM)to mitigate the“black box”effect often associated with deep learning models and to explore the mechanisms by which a CNN classifies various sparse SAR targets.Initially,we apply a novel bidirectional iterative soft thresholding(BiIST)algorithm to generate sparse images of superior quality compared to those produced by traditional matched filtering(MF)techniques.Subsequently,we pretrain multiple shallow CNNs on a simulated SAR image dataset.Using the sparse SAR dataset as input for the CNNs,we assess the efficacy of transfer learning in sparse SAR target classification and suggest the integration of TL-IL-CNN to enhance the classification accuracy further.Finally,Grad-CAM is utilized to provide visual explanations for the predictions made by the classification framework.The experimental results on the MSTAR dataset reveal that the proposed TL-IL-CNN achieves nearly 90%classification accuracy with only 20%of the training data required under standard operating conditions(SOC),surpassing typical deep learning methods such as vision Transformer(ViT)in the context of small samples.Remarkably,it even presents better performance under extended operating conditions(EOC).Furthermore,the application of Grad-CAM elucidates the CNN’s differentiation process among various sparse SAR targets.The experiments indicate that the model focuses on the target and the background can differ among target classes.The study contributes to an enhanced understanding of the interpretability of such results and enables us to infer the classification outcomes for each category more accurately.展开更多
We present a kind of large effective aperture metalens based on an optical sparse aperture(OSA) system. Each subaperture of the system is a metalens, which is comprised of just a thin Au film with patterned subwavelen...We present a kind of large effective aperture metalens based on an optical sparse aperture(OSA) system. Each subaperture of the system is a metalens, which is comprised of just a thin Au film with patterned subwavelength rectangular annular arrays on a Si O2 substrate and has a numerical aperture of 0.46 with a diameter of 21.6 μm.Ring6 design was selected to enlarge the effective aperture and enhance the spatial resolution. Compared with the absent mid-frequency and high-frequency modulation transfer function of individual metalens, Ring6 can offer a full-frequency band and show a better restored image quality by using Tikhonov regularization.展开更多
Cross-range scaling plays an important role in the inverse synthetic aperture radar(ISAR) imaging. Many of the published cross-range scaling algorithms are based on the fast Fourier transformation(FFT). However, the F...Cross-range scaling plays an important role in the inverse synthetic aperture radar(ISAR) imaging. Many of the published cross-range scaling algorithms are based on the fast Fourier transformation(FFT). However, the FFT technique is resolution limited, so that the FFT-based algorithms will fail in the rotation velocity(RV) estimation of the slow rotation target. In this paper,we propose an accurate cross-range scaling algorithm based on the multiple signal classification(MUSIC) method. We first select some range bins with the mono-component linear frequency modulated(LFM) signal model. Then, we dechirp the signal of each selected range bin into the form of sinusoidal signal, and utilize the super-resolution MUSIC technique to accurately estimate the frequency. After processing all the range bins, a linear relationship related to the RV can be obtained. Eventually, the ISAR image can be scaled. The proposal can precisely estimate the small RV of the slow rotation target with low computational complexity. Furthermore, the proposal can also be used in the case of cross-range scaling for the sparse aperture data. Experimental results with the simulated and raw data validate the superiority of the novel method.展开更多
Aiming at the problem of resource allocation for digital array radar（ DAR）,a dwell scheduling algorithm is proposed in this paper. Firstly,the integrated priority of different radar tasks is designed,which ensures t...Aiming at the problem of resource allocation for digital array radar（ DAR）,a dwell scheduling algorithm is proposed in this paper. Firstly,the integrated priority of different radar tasks is designed,which ensures that the imaging tasks are scheduled without affecting the search and tracking tasks; Then,the optimal scheduling model of radar resource is established according to the constraints of pulse interleaving; Finally,a heuristic algorithm is used to solve the problem and a sparse-aperture cognitive ISAR imaging method is used to achieve partial precision tracking target imaging. Simulation results demonstrate that the proposed algorithm can both improve the performance of the radar system,and generate satisfactory imaging results.展开更多
In this Letter,we report a Golay3 sparse-aperture telescope newly built in the Key Laboratory of Optical Engineering,Chinese Academy of Sciences and present the experimental results of enhanced resolution.The telescop...In this Letter,we report a Golay3 sparse-aperture telescope newly built in the Key Laboratory of Optical Engineering,Chinese Academy of Sciences and present the experimental results of enhanced resolution.The telescope consisting of 3 collector telescopes of 127 mm diameter can achieve a theoretical resolution corresponding to a monolithic aperture of 245 mm diameter.It is shown by the experimental results that the resolution is improved to 3.33μrad with respect to the diffraction limit of 6.07μrad for a single aperture using the Rayleigh criteria at 632 nm.The compact optical configuration and cophasing approach are also described.展开更多
文摘To realize effective co-phasing adjustment in large-aperture sparse-aperture telescopes,a multichannel stripe tracking approach is employed,allowing simultaneous interferometric measurements of multiple optical paths and circumventing the need for pairwise measurements along the mirror boundaries in traditional interferometric methods.This approach enhances detection efficiency and reduces system complexity.Here,the principles of the multibeam interference process and construction of a co-phasing detection module based on direct optical fiber connections were analyzed using wavefront optics theory.Error analysis was conducted on the system surface obtained through multipath interference.Potential applications of the interferometric method were explored.Finally,the principle was verified by experiment,an interferometric fringe contrast better than 0.4 is achieved through flat field calibration and incoherent digital synthesis.The dynamic range of the measurement exceeds 10 times of the center wavelength of the working band(1550 nm).Moreover,a resolution better than one-tenth of the working center wavelength(1550 nm)was achieved.Simultaneous three-beam interference can be achieved,leading to a 50%improvement in detection efficiency.This method can effectively enhance the efficiency of sparse aperture telescope co-phasing,meeting the requirements for observations of 8-10 m telescopes.This study provides a technological foundation for observing distant and faint celestial objects.
基金This work was supported by the National Natural Science Foundation of China(61871146).
文摘This paper concentrates on super-resolution imaging of the ship target under the sparse aperture situation.Firstly,a multi-static configuration is utilized to solve the coherent processing interval(CPI)problem caused by the slow-speed motion of ship targets.Then,we realize signal restoration and image reconstruction with the alternating direction method of multipliers(ADMM).Furthermore,we adopt the interferometric technique to produce the three-dimensional(3D)images of ship targets,namely interferometric inverse synthetic aperture radar(InISAR)imaging.Experiments based on the simulated data are utilized to verify the validity of the proposed method.
基金supported in part by the National Natural Science Foundation(Nos.62271248,62401256)in part by the Natural Science Foundation of Ji-angsu Province(Nos.BK20230090,BK20241384)in part by the Key Laboratory of Land Satellite Remote Sens-ing Application,Ministry of Natural Resources of China(No.KLSMNR-K202303)。
文摘In recent years,deeps learning has been widely applied in synthetic aperture radar(SAR)image processing.However,the collection of large-scale labeled SAR images is challenging and costly,and the classification accuracy is often poor when only limited SAR images are available.To address this issue,we propose a novel framework for sparse SAR target classification under few-shot cases,termed the transfer learning-based interpretable lightweight convolutional neural network(TL-IL-CNN).Additionally,we employ enhanced gradient-weighted class activation mapping(Grad-CAM)to mitigate the“black box”effect often associated with deep learning models and to explore the mechanisms by which a CNN classifies various sparse SAR targets.Initially,we apply a novel bidirectional iterative soft thresholding(BiIST)algorithm to generate sparse images of superior quality compared to those produced by traditional matched filtering(MF)techniques.Subsequently,we pretrain multiple shallow CNNs on a simulated SAR image dataset.Using the sparse SAR dataset as input for the CNNs,we assess the efficacy of transfer learning in sparse SAR target classification and suggest the integration of TL-IL-CNN to enhance the classification accuracy further.Finally,Grad-CAM is utilized to provide visual explanations for the predictions made by the classification framework.The experimental results on the MSTAR dataset reveal that the proposed TL-IL-CNN achieves nearly 90%classification accuracy with only 20%of the training data required under standard operating conditions(SOC),surpassing typical deep learning methods such as vision Transformer(ViT)in the context of small samples.Remarkably,it even presents better performance under extended operating conditions(EOC).Furthermore,the application of Grad-CAM elucidates the CNN’s differentiation process among various sparse SAR targets.The experiments indicate that the model focuses on the target and the background can differ among target classes.The study contributes to an enhanced understanding of the interpretability of such results and enables us to infer the classification outcomes for each category more accurately.
基金partially supported by the Intergovernmental International Cooperation Program in Science and Technology Innovation (No. 2016YFE0110600)International Science&Technology Cooperation Program of Shanghai (No. 16520710500)+4 种基金National Natural Science Foundation of China (No. 61805264)Youth Innovation Promotion Association CASScience and Technology Commission of Shanghai Municipality(No. 17YF1429500)Shanghai Sailing Program(No. 18YF1426500)University Scientific Research Foundation of Jiangsu Province (No. KJS1713)
文摘We present a kind of large effective aperture metalens based on an optical sparse aperture(OSA) system. Each subaperture of the system is a metalens, which is comprised of just a thin Au film with patterned subwavelength rectangular annular arrays on a Si O2 substrate and has a numerical aperture of 0.46 with a diameter of 21.6 μm.Ring6 design was selected to enlarge the effective aperture and enhance the spatial resolution. Compared with the absent mid-frequency and high-frequency modulation transfer function of individual metalens, Ring6 can offer a full-frequency band and show a better restored image quality by using Tikhonov regularization.
基金supported by the National Natural Science Foundation of China (61871146,61622107)the China Scholarship Council(201906120113)。
文摘Cross-range scaling plays an important role in the inverse synthetic aperture radar(ISAR) imaging. Many of the published cross-range scaling algorithms are based on the fast Fourier transformation(FFT). However, the FFT technique is resolution limited, so that the FFT-based algorithms will fail in the rotation velocity(RV) estimation of the slow rotation target. In this paper,we propose an accurate cross-range scaling algorithm based on the multiple signal classification(MUSIC) method. We first select some range bins with the mono-component linear frequency modulated(LFM) signal model. Then, we dechirp the signal of each selected range bin into the form of sinusoidal signal, and utilize the super-resolution MUSIC technique to accurately estimate the frequency. After processing all the range bins, a linear relationship related to the RV can be obtained. Eventually, the ISAR image can be scaled. The proposal can precisely estimate the small RV of the slow rotation target with low computational complexity. Furthermore, the proposal can also be used in the case of cross-range scaling for the sparse aperture data. Experimental results with the simulated and raw data validate the superiority of the novel method.
基金Supported by the National Natural Science Foundation of China(61471386)
文摘Aiming at the problem of resource allocation for digital array radar（ DAR）,a dwell scheduling algorithm is proposed in this paper. Firstly,the integrated priority of different radar tasks is designed,which ensures that the imaging tasks are scheduled without affecting the search and tracking tasks; Then,the optimal scheduling model of radar resource is established according to the constraints of pulse interleaving; Finally,a heuristic algorithm is used to solve the problem and a sparse-aperture cognitive ISAR imaging method is used to achieve partial precision tracking target imaging. Simulation results demonstrate that the proposed algorithm can both improve the performance of the radar system,and generate satisfactory imaging results.
基金supported by the National Natural Science Foundation of China(No.61205144)the Research Project of National University of Defense Technology(No.JC13-07-01)the Key Laboratory of High Power Laser and Physics,CAS
文摘In this Letter,we report a Golay3 sparse-aperture telescope newly built in the Key Laboratory of Optical Engineering,Chinese Academy of Sciences and present the experimental results of enhanced resolution.The telescope consisting of 3 collector telescopes of 127 mm diameter can achieve a theoretical resolution corresponding to a monolithic aperture of 245 mm diameter.It is shown by the experimental results that the resolution is improved to 3.33μrad with respect to the diffraction limit of 6.07μrad for a single aperture using the Rayleigh criteria at 632 nm.The compact optical configuration and cophasing approach are also described.
