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Moving target detection in the cepstrum domain for passive coherent location(PCL) radar

Frontiers of information technology & Electronic Engineering 2015年第9期16卷 785-795页

作者： Ji-chuan LI Xiao-de LU Hui ZHANG Peng-cheng YANG Yu LIU Mao-sheng XIANG Science and Technology on Microwave Imaging Laboratory Institute of Electronics Chinese Academy of Sciences University of Chinese Academy of Sciences Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of ElectronicsChinese Academy of Sciences

A cepstrum moving target detection （CEPMTD） algorithm based on cepstrum techniques is proposed for passive coherent location （PCL） radar systems. The primary cepstrum techniques are of great success in recognizing the arrival times of static target echoes. To estimate the Doppler frequencies of moving targets, we divide the radar data into a large number of seg- ments, and reformat these segments into a detection matrix. Applying the cepstrum and the Fourier transform to the fast and slow time dimensions respectively, we can obtain the range information and Doppler information of the moving targets. Based on the CEPMTD outlined above, an improved CEPMTD algorithm is proposed to improve the detection performance. Theoretical analyses show that only the target＇s peak can be coherently added. The performance of the improved CEPMTD is initially vali- dated by simulations, and then by experiments. The simulation results show that the detection performance of the improved CEPMTD algorithm is 13.3 dB better than that of the CEPMTD algorithm and 6.4 dB better than that of the classical detection algorithm based on the radar cross ambiguity function （CAF）. The experiment results show that the detection performance of the improved CEPMTD algorithm is 1.63 dB better than that of the radar CAF.

关键词： Moving target detection Cepstrum techniques Cross ambiguity fimction （CAF） Passive coherent location （PCL）radar

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Visual analyses of music download history: User studies 22nd

Visual analyses of music download history: User studies

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22nd International Conference on MultiMedia Modeling, MMM 2016

作者： Liu, Dong Zhang, Jingxian CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China Department of Computer Science University of Illinois at Urbana-Champaign ChampaignIL United States

ISBN: (纸本)9783319276700

Users’ download history is a primary data source for analyzing user interests. Recent work has shown that user interests are indeed time varying, and accurate profiling of user interest drifts requires the temporal dynamic analyses. We have proposed a visualization approach to analyzing user interest drifts from the download history, taking music as an example, and studied how to depict the underlying relevances among the downloaded music items to identify the drifts. We designed three new kinds of plots to display the music download history of one user, namely Bean plot, Transitional Pie plot, and Instrument plot. In this paper, we report our conducted user studies that ask normal users to visually analyze the download history of other users in a given realworld data set. User studies are performed in a learning-practice-test workflow. The results demonstrate the feasibility of our visualization design. © Springer International Publishing Switzerland 2016.

关键词： Visualization

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A new fusion algorithm for optical remote sensing data

A new fusion algorithm for optical remote sensing data

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2011 International Symposium on Image and Data Fusion, ISIDF 2011

作者： Yu, Kai Hu, Ni Geng, Xiurui Zhao, Yongchao Tang, Hairong Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Science China Patent Examination Cooperation Center of State Intellectual Property Office 100190 China

ISBN: (纸本)9781457709692

The main goal of data fusion in remote sensing is integrating the respective superiority and complementary information of multi-source data to serve the applications. Most conventional fusion algorithm is oriented to fuse the multispectral (hyperspectral) data with panchromatic data such as IHS fusion, PCA fusion, DWT fusion, Brovey fusion etc. Although these methods have been widely used, they still have a problem that can't maintain the spatial information and spectral information synchronously. In this study, a new fusion algorithm aiming at fusing multispectral data and hyperspectral data was developed in following steps: First, classify the hyperspectral data into groups basing on the wavelength range of each multispectral band;second, process each band group by PCA, and combine all the first components to a new multispectral data;then, separate each original multispectral pixel by the homologous pixel and its eight neighbor pixels in the new multispectral data;Finally, mix the homologous 9 pixels of hyperspectral data by the abundance got by former step. The applying on AVIRIS data 'cuprite' brought by ENVI shows an excellent result and verified the validity. © 2011 IEEE.

关键词： Pixels

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Transform-invariant convolutional neural networks for image classification and search 24

Transform-invariant convolutional neural networks for image ...

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24th ACM Multimedia Conference, MM 2016

作者： Shen, Xu Tian, Xinmei He, Anfeng Sun, Shaoyan Tao, Dacheng CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei Anhui230027 China Centre for Quantum Computation and Intelligent Systems Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia

ISBN: (纸本)9781450336031

Convolutional neural networks (CNNs) have achieved stateof-the-art results on many visual recognition tasks. However, current CNN models still exhibit a poor ability to be invariant to spatial transformations of images. Intuitively, with sufficient layers and parameters, hierarchical combinations of convolution (matrix multiplication and nonlinear activation) and pooling operations should be able to learn a robust mapping from transformed input images to transform-invariant representations. In this paper, we propose randomly transforming (rotation, scale, and translation) feature maps of CNNs during the training stage. This prevents complex dependencies of specific rotation, scale, and translation levels of training images in CNN models. Rather, each convolutional kernel learns to detect a feature that is generally helpful for producing the transforminvariant answer given the combinatorially large variety of transform levels of its input feature maps. In this way, we do not require any extra training supervision or modification to the optimization process and training images. We show that random transformation provides significant improvements of CNNs on many benchmark tasks, including small-scale image recognition, large-scale image recognition, and image retrieval. © 2016 ACM.

关键词： Mathematical transformations

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Deep amended COPERT model for regional vehicle emission prediction

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Science China(information Sciences) 2021年第3期64卷 238-240页

作者： Zhenyi XU Yu KANG Yang CAO Zerui LI Department of Automation University of Science and Technology of China Key Laboratory of Technology in Geo Spatial Information Processing and Application System Chinese Academy of Sciences

Dear editor,The existing methods for regional vehicle emission prediction can be roughly categorized into the classes of classical dispersion models and satellite remote sensing *** plume models, operational street ca...

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A general two-dimensional spectrum based on polynomial range model for Medium-Earth-Orbit Synthetic Aperture Radar signal processing

A general two-dimensional spectrum based on polynomial range...

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2010 2nd International Conference on Signal processing systems, ICSPS 2010

作者： Huang, Lijia Hu, Donghui Ding, Chibiao Qiu, Xiaolan Key Laboratory of Geo-spatial Information Processing and Application System Technology Beijing China Institute of Electronics Chinese Academy of Sciences Beijing China Graduate University Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781424468911

The kth-order polynomial range model (PRM) for Medium-Earth-Orbit (MEO) Synthetic Aperture Radar (SAR) signal processing is proposed, analyzed, and verified in this paper. The coefficients of the PRM are calculated according to the relative state vectors between the satellite and the given target on earth, which implies power series expansion around the beam center crossing time. In addition, the relevant two-dimensional spectrum is deduced out as well by using the principle of stationary phase (POSP) and the series reversion approximation. The accuracy of this spectrum is flexible and limited both by the order of the PRM and the order of the spectrum in an expanded form. Therefore, the PRM-based spectrum is general for MEO SAR with any azimuth resolution at any orbit height. © 2010 IEEE.

关键词： Synthetic aperture radar

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The application of the Alternate Descent Conditional Gradient Method in Tomographic SAR Off-grid Imaging

The Application of the Alternate Descent Conditional Gradien...

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IET International Radar Conference 2023, IRC 2023

作者： Shao, Mingxiao Su, Cikang Zhang, Zhe Zhang, Bingchen The Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology Suzhou Aerospace Information Research Institute Jiangsu Suzhou China Aerospace Information Research Institute Chinese Academy of Sciences Chinese Academy of Sciences Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781839539954

Compressed sensing (CS) technology has been applied to topographic synthetic aperture radar (TomoSAR) imaging due to the sparsity of elevation signals. The traditional CS algorithms discretize the elevation into many grids and assume that the scatterers are located on grids, therefore, the accuracy of the traditional CS algorithms is limited by the off-grid effect. Although there are currently methods to address the off-grid effect, such as atomic norm minimization (ANM), its application is limited by the baseline distribution form of TomoSAR, which only applies to uniformly observed scenarios. Alternating Descent Conditional Gradient (ADCG) has been applied in many fields as a gridless imaging algorithm. In this paper, we apply ADCG to TomoSAR imaging for the first time, achieving the reconstruction of non-uniform baseline observation scenes. The effectiveness of ADCG in TomoSAR imaging was verified by comparing it with iterative soft thresholding (IST) and orthogonal matching pursuit (OMP) through simulation experiments. © The Institution of Engineering & technology 2023.

关键词： Compressed sensing

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Multi-aspect Tomographic SAR Imaging Approach via Distributed Compressed Sensing and Joint Sparsity

Multi-aspect Tomographic SAR Imaging Approach via Distribute...

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2021 CIE International Conference on Radar, Radar 2021

作者： Du, Bang Zhang, Zhe Qiu, Xiaolan Lei, Bin Ding, Chibiao Key Laboratory of Technology in Geo-spatial Information Processing and Application System Beijing China Beijing China University of Chinese Academy of Sciences Beijing China Aerospace Information Research Institute Chinese Academy of Sciences Suzhou China

ISBN: (纸本)9781665498142

Synthetic aperture radar (SAR) tomography (TomoSAR) is a novel technique that enables three-dimensional (3-D) imaging and plays an important role in urban remote sensing by utilizing multiple observations of the same target scene from various baselines. Canonical TomoSAR observations are from a single aspect, which has been well studies already. However, modern SAR sensors such as Unmanned Aerial Vehicle (UAV) allow us to achieve multi-aspect TomoSAR data of the same target scene. This paper proposes a novel framework named 'Multi-aspect TomoSAR,' which takes advantage of rich TomoSAR data from multiple observation aspects. We derive the multi-aspect TomoSAR signal model using distributed compressed sensing (DCS) and adopt a simultaneous sparse approximation algorithm named SOMP to solve the joint sparsity model. Numerical results on synthetic simulated data show that the multi-aspect estimation can provide more accurate estimation, yield a promising perspective. Experimental results on real airborne data will be reported in the journal version of this work later. © 2021 IEEE.

关键词： Distributed compressed sensing Joint sparsity model Multi-aspect TomoSAR

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AN ANISOTROPIC SCATTERING ANALYSIS METHOD BASED ON LIKELIHOOD RATIO USING CIRCULAR SAR DATA

AN ANISOTROPIC SCATTERING ANALYSIS METHOD BASED ON LIKELIHOO...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Fei Teng Wen Hong Yun Lin Bing Han Yanping Wang Wenjie Shen Shanshan Feng Key Laboratory of Technology in Geospatial Information Processing and Application System Chinese Academy of Sciences Beijing China North China University of Technology Beijing China

The scattering of an anisotropic target is aspect dependent. Circular SAR (CSAR) can observe the scattering behavior in different aspect angles. In this paper, we propose an anisotropy scattering analysis method based on the likelihood ratio using CSAR data. CSAR data is used to provide sub-aperture images in different aspect angles. The likelihood ratio is defined as the ratio of the conditional probability under two hypotheses, anisotropic and isotropic. Anisotropic and isotropic scatterings can be discriminated by the value of the likelihood ratio. The scattering direction of the anisotropic scattering can be obtained by using our method too. We use a C-band CSAR data, which is acquired by the Institute of Electronics, Chinese Academy of Sciences (IECAS) to validate our method.

关键词： Apertures Image color analysis Radar polarimetry Trajectory Optical imaging Optical scattering

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Sensitivity analysis of along-track interferometric synthetic aperture radar (ATI-SAR) in the presence of squint

Sensitivity analysis of along-track interferometric syntheti...

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IET International Radar Conference 2013

作者： Hui, Zhang Jun, Hong University of Chinese Academy of Sciences Beijing China Chinese Academy of Sciences Beijing Institute of Electronics China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Technology Beijing China Science and Technology on Microwave Imaging Laboratory Beijing China

ISBN: (纸本)9781849196031

Along-Track Interferometric Synthetic Aperture Radar (ATISAR) has the potential of measuring the radial velocity of the slowly moving target. The accuracy of the radial velocity is mainly determined by the accuracy of the interferometric parameters. Sensitivity equations are good ways of analysing the impacts of system parameters for interferometric SAR. Former sensitivity analysis for ATI-SAR has been mainly focused on the case of no squint angle. In this paper, we explain the necessity of analysing the sensitivity equations in the presence of squint angle. Then the expression of the radial velocity in the presence of squint is derived and the sensitivity equations are obtained. Finally, the sensitivity of some interferometric parameters and the impact of squint angles are analysed and simulated for a particular accuracy requirement.

关键词： Sensitivity analysis

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