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Frontiers of Information Technology & Electronic Engineering 2024年第5期25卷 742-754页

作者： Zhenkai ZHANG Xiaoke SHANG Yue XIAO Ocean College Jiangsu University of Science and TechnologyZhenjiang 212003China

Orthogonal time-frequency space(OTFS)is a new modulation technique proposed in recent years for high Doppler wireless *** solve the parameter estimation problem of the OTFS-integrated radar and communications system,we propose a parameter estimation method based on sparse reconstruction preprocessing to reduce the computational effort of the traditional weighted subspace fitting(WSF)***,an OTFS-integrated echo signal model is ***,the echo signal is transformed to the time domain to separate the target angle from the range,and the range and angle of the detected target are coarsely estimated by using the sparse reconstruction ***,the WSF algorithm is used to refine the search with the coarse estimate at the center to obtain an accurate *** simulations demonstrate the effectiveness and superiority of the proposed parameterestimation algorithm.

关键词： Integrated radar and communications system Orthogonal time-frequency space Target parameter estimation sparse reconstruction Weighted subspace fitting

sparse reconstruction Method for DOA Estimation Based on Dynamic Dictionary and Negative Exponent Penalty

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Chinese Journal of Electronics 2018年第2期27卷 386-392页

作者： QIAN Tong CUI Wei SHEN Qing School of Information and Electronic Beijing Institute of Technology

This paper proposes a novel sparse representation method for direction of arrival estimation based on dynamic dictionary and negative exponent penalty. The dynamic dictionary can eliminate the off-grid effect and the negative exponent penalty is capable of strengthening the sparse constraint to improve the performance. The basis is regarded as a part of the optimal target and the cross iteration is utilized to jointly update the dictionary and sparse support in this method. Based on the propositions of the penalty function, the penalty function is designed to replace of 1 norm because of its unbiasedness and stronger sparse constraint. The regularization parameter is simplified as a constant due to pre-white process, which greatly extends the application range of the proposed method. The simulation results show that the proposed method can efficiently reduce the off-grid effect and the over-complete rate of the original dictionary. Compared with the conventional sparse representation methods, it has better performance and lower computation complexity.

关键词： Direction of arrival(DOA) sparse reconstruction Dynamic dictionary Penalty function

Compressed sensing sparse reconstruction for coherent field imaging

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Chinese Physics B 2016年第4期25卷 79-84页

作者：曹蓓罗秀娟张羽刘辉陈明徕 Xi'an Institute of Optics and Precision Mechanics Chinese Academy of Sciences

Return signal processing and reconstruction plays a pivotal role in coherent field imaging, having a significant in- fluence on the quality of the reconstructed image. To reduce the required samples and accelerate the sampling process, we propose a genuine sparse reconstruction scheme based on compressed sensing theory. By analyzing the sparsity of the received signal in the Fourier spectrum domain, we accomplish an effective random projection and then reconstruct the return signal from as little as 10% of traditional samples, finally acquiring the target image precisely. The results of the numerical simulations and practical experiments verify the correctness of the proposed method, providing an efficient processing approach for imaging fast-moving targets in the future.

关键词： coherent field imaging computational imaging sparse reconstruction compressed sensing

A FAST CONVERGING sparse reconstruction ALGORITHM IN GHOST IMAGING

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Journal of Electronics(China) 2012年第6期29卷 617-620页

作者： Li Enrong Chen Mingliang Gong Wenlin Wang Hui Han Shensheng Key Laboratory for Quantum Optics and Center for Cold Atom Physics of CAS Shanghai Institute of Optics and Fine MechanicsChinese Academy of Sciences

A fast converging sparse reconstruction algorithm in ghost imaging is presented. It utilizes total variation regularization and its formulation is based on the Karush-Kuhn-Tucker (KKT) theorem in the theory of convex optimization. Tests using experimental data show that, compared with the algorithm of Gradient Projection for sparse reconstruction (GPSR), the proposed algorithm yields better results with less computation work.

关键词： Compressive sensing Ghost Imaging (GI) sparse reconstruction Karush-Kuhn-Tucker (KKT) condition Gradient projection

sparse reconstruction of regional ionospheric tomography based on Beidou Ground Based Augmentation System

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Sparse reconstruction of regional ionospheric tomography bas...

sparse Reconstructive Evidential Clustering for Multi-View Data

第十一届中国卫星导航年会

作者： Yun Sui Haiyang Fu Denghui Wang Shaojun Feng Zonghua Ding Feng Xu Yaqiu Jin Key Laboratory for Information Science of Electromagnetic Waves (MoE) Fudan University Qianxun Spatial Intelligence Inc. China Research Institute of Radiowave Propagation

Real-time and accurate modeling of three-dimensional(3D) electron density in the ionosphere is important for space navigation and communication technology as well as space weather. This paper investig

关键词： GNSS UPPP sparse reconstruction Compressive Sensing ISR

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 459-473页

作者： Chaoyu Gong Yang You School of Computing National University of SingaporeSingapore

Although many multi-view clustering(MVC) algorithms with acceptable performances have been presented, to the best of our knowledge, nearly all of them need to be fed with the correct number of clusters. In addition, these existing algorithms create only the hard and fuzzy partitions for multi-view objects,which are often located in highly-overlapping areas of multi-view feature space. The adoption of hard and fuzzy partition ignores the ambiguity and uncertainty in the assignment of objects, likely leading to performance degradation. To address these issues, we propose a novel sparse reconstructive multi-view evidential clustering algorithm(SRMVEC). Based on a sparse reconstructive procedure, SRMVEC learns a shared affinity matrix across views, and maps multi-view objects to a 2-dimensional humanreadable chart by calculating 2 newly defined mathematical metrics for each object. From this chart, users can detect the number of clusters and select several objects existing in the dataset as cluster centers. Then, SRMVEC derives a credal partition under the framework of evidence theory, improving the fault tolerance of clustering. Ablation studies show the benefits of adopting the sparse reconstructive procedure and evidence theory. Besides,SRMVEC delivers effectiveness on benchmark datasets by outperforming some state-of-the-art methods.

关键词： Evidence theory multi-view clustering(MVC) optimization sparse reconstruction

一种基于迭代近端投影的被动声纳探测离网格DOA估计方法

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哈尔滨工程大学学报（英文版） 2024年第2期23卷 417-424页

作者：戴泽华张亮韩笑殷敬伟 National Key Laboratory of Underwater Acoustic Technology Harbin Engineering UniversityHarbin 150001China Key Laboratory for Polar Acoustics and Application of Ministry of Education Harbin Engineering UniversityHarbin 150001China College of Underwater Acoustic Engineering Harbin Engineering UniversityHarbin 150001China

Traditional direction of arrival(DOA)estimation methods based on sparse reconstruction commonly use convex or smooth functions to approximate non-convex and non-smooth sparse representation *** approach often introduces errors into the sparse representation model,necessitating the development of improved DOA estimation ***,conventional DOA estimation methods typically assume that the signal coincides with a predetermined ***,in reality,this assumption often does not hold *** likelihood of a signal not aligning precisely with the predefined grid is high,resulting in potential grid mismatch issues for the *** address the challenges associated with grid mismatch and errors in sparse representation models,this article proposes a novel high-performance off-grid DOA estimation approach based on iterative proximal projection(IPP).In the proposed method,we employ an alternating optimization strategy to jointly estimate sparse signals and grid offset parameters.A proximal function optimization model is utilized to address non-convex and non-smooth sparse representation problems in DOA ***,we leverage the smoothly clipped absolute deviation penalty(SCAD)function to compute the proximal operator for solving the *** and sea trial experiments have validated the superiority of the proposed method in terms of higher resolution and more accurate DOA estimation performance when compared to both traditional sparse reconstruction methods and advanced off-grid techniques.

关键词： DOA estimation sparse reconstruction Off-grid model Iterative proximal projection Passive sonar detection

Radar Imaging of Sidelobe Suppression Based on sparse Regularization

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Journal of Computer and Communications 2016年第3期4卷 108-115页

作者： Xiaoxiang Zhu Guanghu Jin Feng He Zhen Dong Institute of Space Electronic and Information Technology School of Electronic Science and Engineering National University of Science and Technology Changsha China

Synthetic aperture radar based on the matched filter theory has the ability of obtaining two-di- mensional image of the scattering areas. Nevertheless, the resolution and sidelobe level of SAR imaging is limited by the antenna length and bandwidth of transmitted signal. However, for sparse signals (direct or indirect), sparse imaging methods can break through limitations of the conventional SAR methods. In this paper, we introduce the basic theory of sparse representation and reconstruction, and then analyze several common sparse imaging algorithms: the greed algorithm, the convex optimization algorithm. We apply some of these algorithms into SAR imaging using RadBasedata. The results show the presented method based on sparse construction theory outperforms the conventional SAR method based on MF theory.

关键词： Matched Filtering sparse Representation sparse reconstruction Convex Optimization Greed Algorithm

Weak Supervised Detection of Abnormal Behavior

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Weak Supervised Detection of Abnormal Behavior