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Undistorted and Consistent Enhancement of Automotive SAR Image via Multi-Segment-Reweighted Regularization

Remote Sensing 2025年第9期17卷

作者： Zhang, Yan Zhang, Bingchen Wu, Yirong Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100190 China Key Laboratory of Technology in Geo-Spatial Information and Processing Application System Beijing100190 China Key Laboratory of Target Cognition and Application Technology Beijing100190 China

In recent years, synthetic aperture radar (SAR) technology has been increasingly explored for automotive applications. However, automotive SAR images generated via matched filter (MF) often exhibit challenges such as noisy backgrounds, sidelobe artifacts, and limited resolution. Sparse regularization methods have the potential to enhance image quality. Nevertheless, conventional unweighted (Formula presented.) regularization methods struggle to address cases with radar cross section (RCS) distributed over a wide dynamic range, often resulting in insufficient sidelobe suppression, amplitude distortion, and inconsistent super-resolution performance. In this paper, we propose a novel reweighted regularization method, termed multi-segment-reweighted regularization (MSR), for automotive SAR image restoration. By introducing a novel weighting scheme, MSR localizes the global scattering point enhancement problem to the mainlobe scale, effectively mitigating sidelobe interference. This localization ensures consistent enhancement capability independent of RCS variations. Furthermore, MSR employs multi-segment regularization to constrain amplitude within the mainlobes, preserving the characteristics of the original response. Correspondingly, a new thresholding function, named Thinner Response Undistorted THresholding (TRUTH), is introduced. An iterative algorithm for enhancing automotive SAR images using MSR is also presented. Real data experiments validate the feasibility and effectiveness of the proposed method. © 2025 by the authors.

关键词： Image segmentation

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A SAR imaging method based on generalized minimax-concave penalty

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Science China(information Sciences) 2019年第2期62卷 233-239页

作者： Zhonghao WEI Bingchen ZHANG Yirong WU University of Chinese Academy of Sciences Key Laboratory of Technology in Geospatial Information Processing and Application Systems Institute of Electronics Chinese Academy of Sciences Institute of Electronics Chinese Academy of Sciences

Dear editor,Sparse signal processing offers a framework for synthetic aperture radar （SAR） imaging [1, 2]. As an efficient tool in sparse signal processing, L1minimization is often used in the reconstruction of SAR images. When implemented in SAR imaging [3–5], L1minimization offers significant improvement in the properties by suppressing the sidelobes and clutter. However, L1minimization is known to be a biased estimator. The L1minimization based algorithms such as the iterative

关键词： SAR A SAR imaging method based on generalized minimax-concave penalty

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RFID Cardinality Estimation Method for Intelligent Warehouse 35

RFID Cardinality Estimation Method for Intelligent Warehouse

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第35届中国控制会议

作者： SHEN Jingjing TAN Xiaobin WU Fan YAN Pei CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China

ISBN: (纸本)9781509009107

With the rapid development of RFID technologies,RFID has been introduced into applications such as supply chain management,inventory control,sampling inspection,3-D positioning and object ***,the reader accesses all the tags in its interrogation region while some applications may only need to identify the tags in a specified area which is smaller than the reader's interrogation *** paper concerns the essential problem of estimating cardinality of tags in the specified *** key novelty of our solution builds on an estimation synopsis that can capture key counting information by moving the reader as well as a simple *** the help of this data structure,a BS can be obtained which only contains the target *** computing the number of 1 in the BS,we can easily get cardinality |E| of the tags in the specified *** conduct extensive experiments to examine this design and the results shows that our solution achieves high *** it not requires any modification of tags and can be implemented with only one reader and some passive RFID tags,the proposed method is easy to deploy in a practical system.

关键词： Cardinality estimation RFID tags algorithm design

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Intertidal area classification with generalized extreme value distribution and Markov random field in quad-polarimetric synthetic aperture radar imagery

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Frontiers of information technology & Electronic Engineering 2019年第2期20卷 253-264页

作者： Ting-ting JIN Xiao-qiang SHE Xiao-lan QIU Bin LEI Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of ElectronicsChinese Academy of Sciences University of Chinese Academy of Sciences Huawei Software Technologies Co. Ltd.

Classification of intertidal area in synthetic aperture radar(SAR) images is an important yet challenging issue when considering the complicatedly and dramatically changing features of tidal fluctuation. The difficulty of intertidal area classification is compounded because a high proportion of this area is frequently flooded by water, making statistical modeling methods with spatial contextual information often ineffective. Because polarimetric entropy and anisotropy play significant roles in characterizing intertidal areas, in this paper we propose a novel unsupervised contextual classification algorithm. The key point of the method is to combine the generalized extreme value(GEV) statistical model of the polarization features and the Markov random field(MRF) for contextual smoothing. A goodness-of-fit test is added to determine the significance of the components of the statistical model. The final classification results are obtained by effectively combining the results of polarimetric entropy and anisotropy. Experimental results of the polarimetric data obtained by the Chinese Gaofen-3 SAR satellite demonstrate the feasibility and superiority of the proposed classification algorithm.

关键词： Intertidal classification Polarimetric synthetic aperture radar Finite mixture model Markov random field Generalized extreme value model

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Planar near-field to far-field transformation for massive MIMO systems 34

Planar near-field to far-field transformation for massive MI...

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第三十四届中国控制会议

作者： HAN Xiangzi TAN Xiaobin WU Fan CAS Key laboratory of Technology in Geo-spatial Information Processing and Application System School of Information Science and Technology University of Science and Technology of China

ISBN: (纸本)9781467374439

The massive or large scaled multiple input multiple output(MIMO) systems have gained huge consideration due to high achievable data rates, reliable system performance and advantaged energy efficiency for future wireless networks. For such systems, we should have a keen knowledge of the channel model, the ways of configuration, spectral efficiency and so on. The obtaining of the far-field pattern in the massive MIMO system from measurements made in the near-field has attracted widespread attention recently. In this article, two basic techniques for accomplishing this have been proposed. In the first technique, the NTFF(near-field to far-field) transformation is based on the equivalence principle. While in the second technique, it is based on the plane wave spectrum(PWS) expansion. Both of the methods have their own advantages for the massive MIMO systems. One can drastically reduce the computation time and the other can make enormous reduction in the storage consumption. Experimental results show that the two transformation techniques are proved to be feasible and they are consistent with each other.

关键词： massive MIMO,NTFF transformation,the equivalence principle,PWS expansion

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Star extraction by star sensors for daytime images affected by atmospheric turbulence

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Chinese Journal of Aeronautics 2025年

作者： Wanxiang GOU Yinhu ZHAN Chonghui LI Shuai TONG Yong ZHENG Yuan YANG Hanxu LI College of Geospatial Information Information Engineering University Zhengzhou 450000 China Key Laboratory of Smart Earth Beijing 100029 China Key Laboratory of Spatiotemporal Perception and Intelligent Processing Ministry of Natural Resources Zhengzhou 450000 China Collaborative Innovation Center of BDS Navigation Application Technology Henan Province Zhengzhou 450000 China

Daytime star images captured by dedicated near-space star sensors are characterized by short exposures, high noise, and low Signal-to-Noise Ratios (SNRs). Such imaging is also affected by atmospheric turbulence, causing optical phenomena, such as scintillation, distortion, and jitter. This causes difficulty in recording high-precision star images during the daytime. This study proposes an adaptive star point extraction method based on dynamically predicting stars’ positions. First, it predicts the approximate position of stars based on the star catalog, sensor attitude, observation time, and other information, improving the extraction accuracy. Second, it employs a regional SNR sorting method that adaptively selects star images with higher SNRs, suppressing the scintillation effect and enhancing the SNR of star images. Third, depending on the star’s motion trajectory characteristics on the image plane, it utilizes the centroid smoothing method for extraction, thus overcoming the impact of star drift. Field experiments demonstrate that the proposed method can effectively overcome star scintillation, drift, and irregular imaging caused by atmospheric turbulence, achieving a 100% success rate. Moreover, the extraction accuracy improves by more than 80% compared to traditional adaptive methods, attaining a value of 0.05 pixels (0.5’’), thereby meeting the requirements of daytime astronomical attitude determination and positioning.

关键词： Astronomical navigation Star sensor Daytime star image Atmospheric turbulence Star extraction Centroid accuracy

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Multi-dimensional graph linear canonical transform

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Digital Signal processing: A Review Journal 2025年 165卷

作者： Li, Na Zhang, Zhichao School of Mathematics and Statistics Center for Applied Mathematics of Jiangsu Province Jiangsu International Joint Laboratory on System Modeling and Data Analysis Nanjing University of Information Science and Technology Nanjing 210044 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai 200240 China Key Laboratory of Computational Science and Application of Hainan Province Haikou 571158 China

Multi-dimensional (M-D) graph signals represent fundamental data structures in various practical applications, including digital imaging systems, sensor network telemetry, and meteorological observation records. The development of efficient transformation methodologies for processing such M-D graph signals within the linear canonical transform domain continues to present significant challenges. This paper introduces two novel transforms: the two-dimensional graph linear canonical transform based on central discrete dilated Hermite functions (2-D CDDHFs-GLCT) and the two-dimensional graph linear canonical transform based on chirp multiplication-chirp convolution-chirp multiplication decomposition (2-D CM-CC-CM-GLCT). These foundational constructs are extended to the multi-dimensional domain, resulting in M-D CDDHFs-GLCT and M-D CM-CC-CM-GLCT. In terms of computational complexity, additivity and reversibility, M-D CDDHFs-GLCT and M-D CM-CC-CM-GLCT are compared. Theoretical analyses reveal that the M-D CM-CC-CM-GLCT algorithm achieves substantial computational complexity reduction compared to its CDDHFs-based counterpart. Numerical simulations further establish that while maintaining comparable additivity performance, the M-D CM-CC-CM-GLCT exhibits enhanced reversibility characteristics. To validate practical applicability, the proposed M-D GLCT framework is implemented in data compression scenarios. Experimental results demonstrating superior algorithmic efficiency and implementation effectiveness compared to multi-dimensional graph fractional Fourier transform. © 2025 Elsevier Inc.

关键词： Data compression Graph signal processing Linear canonical transform Multi-dimensional signal processing

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An azimuth resample method for bistatic SAR motion compensation 8

An azimuth resample method for bistatic SAR motion compensat...

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8th European Conference on Synthetic Aperture Radar, EUSAR 2010

作者： Qiu, Xiaolan Han, Bin Meng, Dadi Hu, Donghui Ding, Chibiao Key Laboratory of Technology in Geo-spatial Information Processing and Application System IECAS China China

ISBN: (纸本)9783800732722

Motion compensation is an indispensable step for generating a high quality image in the airbore 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 mono static SAR and bistatic SAR is pointed out. Finally, simulated results show the validity of the method. © VDE VERLAG GMBH · Berlin · Offenbach.

关键词： Motion compensation

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Vibration Error Compensation Imaging Algorithm for Arc Array SAR Besed on Parameter Estimation 6

Vibration Error Compensation Imaging Algorithm for Arc Array...

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6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

作者： Dong, Yifan Wu, Congfei Xu, Wei Huang, Pingping Tan, Weixian Hong, Wen College of Information Engineering Inner Mongolia University of Technology China Inner Mongolia Key Laboratory of Radar Technology and Application Hohhot China Institute of Electronics Chinese Academy of Sciences China Key Laboratory of Technology in Geospatial Information Processing and Application System Beijing China

ISBN: (纸本)9781728129129

Arc array synthetic aperture radar (SAR) is a novel array imaging system for wide-area observation, with wide observation range and high resolution. Arc array synthetic aperture radar uses W-band as carrier signal, and W-band wavelength is short. The small high-frequency vibration of the helicopter-borne platform will cause significant changes in the phase of the echo signal, which will seriously deteriorate the imaging performance of arc array synthetic aperture radar. Based on the high order approximate imaging algorithm of arc array radar, this paper proposes a vibration phase error compensation imaging algorithm based on Short-time Fourier transform (STFT) parameter estimation. The algorithm uses the high-order approximation imaging algorithm of curved array radar to perform high-order approximation of the slant range model, compensates the range cell migration (RCM) and range azimuth coupling in the two-dimensional frequency domain, and finally realizes the vibration error compensation in the range Doppler domain. The simulation results of the lattice target verify the effectiveness of the imaging method. © 2019 IEEE.

关键词： Synthetic aperture radar

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Non-negative matrix factorization based unmixing for principal component transformed hyperspectral data

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Frontiers of information technology & Electronic Engineering 2016年第5期17卷 403-412页

作者： Xiu-rui GENG Lu-yan JI Kang SUN Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing 100190 China MOE Key Laboratory for Earth System Modeling Center for Earth System Science Tsinghua University Beijing 100084 China The 54th Research Institute of China Electronics Technology Group Corporation Shijiazhuang 050000 China

Non-negative matrix factorization(NMF) has been widely used in mixture analysis for hyperspectral remote sensing. When used for spectral unmixing analysis, however, it has two main shortcomings:(1) since the dimensionality of hyperspectral data is usually very large, NMF tends to suffer from large computational complexity for the popular multiplicative iteration rule;(2) NMF is sensitive to noise(outliers), and thus the corrupted data will make the results of NMF meaningless. Although principal component analysis(PCA) can be used to mitigate these two problems, the transformed data will contain negative numbers, hindering the direct use of the multiplicative iteration rule of NMF. In this paper, we analyze the impact of PCA on NMF, and find that multiplicative NMF can also be applicable to data after principal component transformation. Based on this conclusion, we present a method to perform NMF in the principal component space, named ‘principal component NMF'(PCNMF). Experimental results show that PCNMF is both accurate and time-saving.

关键词： Non-negative matrix factorization(NMF) Principal component analysis(PCA) Endmember Hyperspectral

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