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14th European Conference on Synthetic Aperture Radar, EUSAR 2022

作者： Xua, Zhonqiu Zhoua, Guoru Zhanga, Bingchen Zhang, Zhe Wu, Yirong Key Laboratory of Spatial Information Processing and Application System Technology Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Aerospace Information Research Institute Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783800758234

Synthetic aperture radar (SAR) imaging under the sparse constraint is a developing SAR imaging scheme that emerged in the recent decade. In sparse SAR imaging, the sparsity can be regarded as a priori information, used to enhance different types of imaging features, among which `1-norm and the total variation (TV)-norm are two widely used constraints in the reconstruction model corresponding to the features of point targets and distributed targets respectively. However, `1 regularization often generates a biased estimation and the TV regularization always over-smooths isolated point targets in the scene. In this paper, we propose the nonconvex-nonlocal total variation (NLTV) regularization, which can realize the joint feature enhancement of sparse SAR imaging. The modified variable splitting-Alternating direction method of multipliers (VS-ADMM) is introduced to solve the compound regularization. The performance of the proposed method is verified using simulated data and Gaofen-3 (GF-3) SAR data. © 2022 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Synthetic aperture radar

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IMPROVED CONJUGATE GRADIENT BUNDLE ADJUSTMENT of DUNHUANG WALL PAINTING IMAGES

IMPROVED CONJUGATE GRADIENT BUNDLE ADJUSTMENT of DUNHUANG WA...

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ISPRS geospatial Week 2017

作者： Hu, K. Huang, X. You, H. Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences No. 19 North 4th Ring Road West Beijing100190 China Wuhan Engineering Science and Technology Institute Jiangda Road 30 Wuhan Hubei430019 China

Bundle adjustment with additional parameters is identified as a critical step for precise orthoimage generation and 3D reconstruction of Dunhuang wall paintings. Due to the introduction of self-calibration parameters and quasi-planar constraints, the structure of coefficient matrix of the reduced normal equation is banded-bordered, making the solving process of bundle adjustment complex. In this paper, Conjugate Gradient Bundle Adjustment (CGBA) method is deduced by calculus of variations. A preconditioning method based on improved incomplete Cholesky factorization is adopt to reduce the condition number of coefficient matrix, as well as to accelerate the iteration rate of CGBA. Both theoretical analysis and experimental results comparison with conventional method indicate that, the proposed method can effectively conquer the ill-conditioned problem of normal equation and improve the calculation efficiency of bundle adjustment with additional parameters considerably, while maintaining the actual accuracy. © Authors 2017.

关键词： Calculus of variations Conjugate gradient bundle adjustment (CGBA) Dunhuang wall painting images Improved incomplete Cholesky factorization Space complexity Time complexity

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Journal of Computational information systems 2014年第17期10卷 7259-7267页

作者： Qi, Xiang Wu, Wei Huang, Yu Huang, Tinglei Fu, Kun Wang, Hongqi Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Institute of Electronics University of Chinese Academy of Sciences Beijing China University of Science and Technology of China Hefei China

In this paper we propose the rating Correlated Topic Model for rating-based collaborative filtering, which improves the performance of the state-of-the-art Latent Semantic Models in two aspects: (1), making the prediction accuracy more robust to the topic number K;(2), improving the recommendation quality for users with few existed ratings. We achieve our goals by employing the Logistic Normal distribution to capture the correlation between latent topics following the Correlated Topic Model, as well as modifying the generative process to meet the requirement of rating-based collaborative filtering. We derive a parameter estimation algorithm based on variational inference for the proposal. Experiment results on the Movielens data set demonstrate our model's advantages on both referred aspects.

关键词： Collaborative filtering

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A cloaking-based approach to protect location privacy in location-based services 33

A cloaking-based approach to protect location privacy in loc...

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Proceedings of the 33rd Chinese Control Conference, CCC 2014

作者： Zheng, Jiangyu Tan, Xiaobin Zou, Cliff Niu, Yukun Zhu, Jin CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei230026 China Department of Automation University of Science and Technology of China Hefei230026 China Department of Electrical Engineering and Computer Science University of Central Florida OrlandoFL32816-2362 United States

ISBN: (纸本)9789881563842

With the widespread use of mobile devices, the location-based service (LBS) applications become increasingly popular, which introduces the new security challenge to protect user's location privacy. On one hand, a user expects to report his own location as far as possible away from his real location to protect his location privacy. On the other hand, in order to obtain high quality of service (QoS), users are required to report their locations as accurate as possible. To achieve the dedicated tradeoff between privacy requirement and QoS requirement, we propose a novel approach based on cloaking technique. We also discuss the disadvantage of the traditional general system model and propose an improved model. The basic idea of our approach is to select a sub-area from the generated cloaking area as user's reported location. The sub-area may not contain a user's real location, which prevents an adversary from performing attack with side information. Specifically, by defining an objective function with a novel location privacy metric and a QoS metric, we are able to convert the privacy issue to an optimization problem. Then, location privacy metric and QoS metric are given. To reduce the complexity of the optimization, a heuristic algorithm is proposed. Through privacy-preserving analysis and comparison with related work [8], we demonstrate the effectiveness and efficiency of our approach. © 2014 TCCT, CAA.

关键词： Location based services

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GRIDLESS DOA ESTIMATION FOR AUTOMOTIVE RADARS WITH VARIOUS ARRAY geoMETRIES: THE NON-VANDERMONDE ATOMIC SOFT THRESHOLDING APPROACH

GRIDLESS DOA ESTIMATION FOR AUTOMOTIVE RADARS WITH VARIOUS A...

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IET International Radar Conference 2023, IRC 2023

作者： Gao, Silin Wang, Muhan Zhang, Zhe Zhang, Bingchen Wu, Yirong Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou215123 China Suzhou Aerospace Information Research Institute Suzhou215123 China

ISBN: (纸本)9781839539954

This paper is centered on gridless direction of arrival (DoA) estimation for single-snapshot data collected by non-uniform linear arrays (NLAs) in the context of automotive applications. While recent single-snapshot DoA estimation algorithms rooted in grid-based compressed sensing (CS) offer super-resolution capabilities, they are notably sensitive to discrepancies between the assumed sparsity basis and the actual basis. Furthermore, the existing atomic soft thresholding (AST) algorithm, a promising gridless sparse recovery technique within the Toeplitz model, is constrained to uniform linear arrays (ULAs) exhibiting Vandermonde structure in their array manifolds. However, in automotive scenarios, it becomes imperative to efficiently apply gridless DoA estimation to NLAs featuring non-Vandermonde array manifolds. In this paper, we introduce the non-Vandermonde atomic soft thresholding (NVAST) algorithm and employ it for DoA estimation in NLAs. The novel algorithm establishes a link between Vandermonde and non-Vandermonde atoms by decomposing non-Vandermonde vectors and addressing them using semi-definite programming (SDP). Simulation and measurement experiments conducted with automotive radars underscore the remarkable performance of our proposed method. © The Institution of Engineering & technology 2023.

关键词： Automotive radar

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AN AUTOFOCUS NETWORK FOR MULTI-CHANNEL PHASE ERRORS WITH application TO TOMOSAR IMAGING

AN AUTOFOCUS NETWORK FOR MULTI-CHANNEL PHASE ERRORS WITH APP...

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IET International Radar Conference 2023, IRC 2023

作者： Wang, Muhan Gao, Silin Zhang, Zhe Qiu, Xiaolan Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou215123 China Suzhou Aerospace Information Research Institute Suzhou215123 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781839539954

Synthetic aperture radar (SAR) tomography (TomoSAR) has garnered significant attention due to its capability for three-dimensional reconstruction. Compressed sensing (CS) methods are widely employed to address the TomoSAR inversion challenge. Nevertheless, practical applications reveal phase errors among different channels, resulting in defocusing and blurring when relying solely on CS for 3D reconstruction. Current state-of-the-art autofocus techniques suffer from prohibitive computational complexity, limiting their applicability to large-scale 3D imaging. In pursuit of efficient TomoSAR 3-D autofocusing, we proposed ASAMP-Net, an innovative deep unfolding network. Operating within a two-step framework, each layer comprises two stages: phase error estimation and iterative scattering coefficient reconstruction using the sparse adaptive matching pursuit (SAMP) algorithm. Additionally, phase error estimation is obtained through mathematical derivation, while challenges associated with fixed sparsity and limited efficiency in conventional methods are mitigated through deep learning techniques. Simulation experiments and real data validation affirm the effectiveness and superiority of the proposed method. © The Institution of Engineering & technology 2023.

关键词： Radar imaging

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GRIDLESS TOMOGRAPHIC SAR IMAGING BASED ON ACCELERATED ATOMIC NORM MINIMIZATION WITH EFFICIENCY

GRIDLESS TOMOGRAPHIC SAR IMAGING BASED ON ACCELERATED ATOMIC...

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2022 International Conference on Radar systems, RADAR 2022

作者： Gao, Silin Zhang, Zhe Zhang, Bingchen Wu, Yirong Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou215123 China Suzhou Aerospace Information Research Institute Suzhou215123 China

ISBN: (纸本)9781839537776

Synthetic aperture radar (SAR) tomography (TomoSAR) enables the reconstruction and three-dimensional (3D) localization of targets based on multiple two-dimensional (2D) observations of the same scene. The resolving along the elevation direction can be treated as a line spectrum estimation problem. However, traditional super-resolution spectrum estimation algorithms require multiple snapshots and uncorrelated targets. Meanwhile, as the most popular TomoSAR imaging method in modern years, compressed sensing (CS) based methods suffer from the gridding mismatch effect which markedly degrades the imaging performance. As a gridless CS approach, atomic norm minimization can avoid the gridding effect but requires enormous computing resources. Addressing the above issues, this paper proposes an improved fast ANM algorithm to TomoSAR elevation focusing by introducing the IVDST-ANM algorithm, which reduces the huge computational complexity of the conventional time-consuming semi-positive definite programming (SDP) by the iterative Vandermonde decomposition and shrinkage-thresholding (IVDST) approach, and retains the benefits of ANM in terms of gridless imaging and single snapshot recovery. We conducted experiments using simulated data to evaluate the performance of the proposed method, and reconstruction results of an urban area from the SARMV3D-Imaging 1.0 dataset are also presented. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Compressed sensing

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Controllability of Indu-Bala product networks

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Neural Networks 2025年 190卷

作者： Qiang Zhang Junjie Huang Bo Liu Housheng Su Alatancang Chen School of Mathematical Sciences Inner Mongolia University Hohhot 010021 China School of Information Engineering Minzu University of China Beijing 100081 China National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan 430074 China School of Mathematical Sciences the Ministry of Education Key Laboratory for Infinite-dimensional Hamiltonian System and Its Algorithm Application Inner Mongolia Normal University Hohhot 010022 China

A composite network formed by factor networks is a new type of complex network model that can describe different types of agents and multiple complicated relationships in complex systems. “Indu-Bala product” of graphs is a new graph operation with symmetry properties and good mathematical expression, which enables one to construct many pairs of respective co-spectral graphs. This work studies the controllability of a composite network formed by factor networks via Indu-Bala product based on Laplacian dynamics. The eigenvalues and eigenvectors of Indu-Bala product networks (IBPNs) can be represented by factors’ eigenvalues and eigenvectors, which can reduce the computational complexity and analytical burden. Based on the PBH test, sufficient and necessary algebraic criterions for controllability of IBPNs are established, which reveal the controllability relationship between entire network and its factors. This work will help one to construct, understand and improve the composite complex network models, and gain insight into the synergistic mechanisms of the networks.

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An Azimuth Resample Method For Bistatic SAR Motion Compensation

An Azimuth Resample Method For Bistatic SAR Motion Compensat...

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8th European Conference on Synthetic Aperture Radar

作者： Xiaolan Qiu Bin Han Dadi Meng Donghui Hu Chibiao Ding Key Laboratory of Technology in Geo-spatial Information Processing and Application System IECAS China Institute of Electronics Chinese Academy of Sciences (IECAS) China

Motion compensation is an indispensable step for generating a high quality image in the airborne SAR system. In bistatic SAR, there are two separate platforms and their velocities cannot keep same all the time. So the echoes are transmitted and received at uniform positions, respectively, which deteriorate the imaging results. In this paper, an azimuth resample method for compensating the velocity variance along the flying direction is promoted, and the difference of azimuth resample between monostatic SAR and bistatic SAR is pointed out. Finally, simulated results show the validity of the method.

关键词： Yttrium Transmitters

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A Hybrid and Explainable Deep Learning Framework for SAR Images

A Hybrid and Explainable Deep Learning Framework for SAR Ima...

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IEEE International Symposium on geoscience and Remote Sensing (IGARSS)

作者： Zhongling Huang Mihai Datcu Zongxu Pan Bin Lei Key Laboratory of Technology in Geo-spatial Information Processing and Application System CAS Remote Sensing Technology Institute (IMF) German Aerospace Center (DLR)

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

Deep learning based patch-wise Synthetic Aperture Radar (SAR) image classification usually requires a large number of labeled data for training. Aiming at understanding SAR images with very limited annotation and taking full advantage of complex-valued SAR data, this paper proposes a general and practical framework for quad-, dual-, and single-polarized SAR data. In this framework, two important elements are taken into consideration: image representation and physical scattering properties. Firstly, a convolutional neural network is applied for SAR image representation. Based on time-frequency analysis and polarimetric decomposition, the scattering labels are extracted from complex SAR data with unsupervised deep learning. Then, a bag of scattering topics for a patch is obtained via topic modeling. By assuming that the generated scattering topics can be regarded as the abstract attributes of SAR images, we propose a soft constraint between scattering topics and image representations to refine the network. Finally, a classifier for land cover and land use semantic labels can be learned with only a few annotated samples. The framework is hybrid for the combination of deep neural network and explainable approaches. Experiments are conducted on Gaofen-3 complex SAR data and the results demonstrate the effectiveness of our proposed framework.

关键词： Deep learning Training Annotations Scattering Image representation Radar polarimetry Synthetic aperture radar

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