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2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： Fang, Linlin Zhong, Lihua Han, Bing Hu, Yuxin Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798331515669

Space-based radar has better coverage and is not limited by the curvature of the earth. Aircraft target tracking is particularly important in space-based radar system. However, the long observation distance and strong nonlinear observation increases the challenge of target tracking. Therefore, we proposed a space-based radar nonlinear tracking method for air targets based on variational Bayesian probability hypothesis density (VB-PHD) filter. The relationship between state and observation is established. Then, the VB method is adopted to deal with uncertainty of measurement noise variances. Besides, the cubature filter is introduced to deal with the strongly nonlinear models, and the recursion process is completed by using the third-order spherical radial volume rule. Finally, simulation scenarios with different measurement noise parameters were constructed, and the tracking result comparisons show that it effectively solves nonlinear problems, and tracking precision can be significantly improved. © 2024 IEEE.

关键词： Space-based radar

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

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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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Stabilization of Time-Delay Markovian Jump systems via Probability Rate Synthesis and State Feedback

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IFAC Proceedings Volumes 2014年第3期47卷 10415-10420页

作者： Shan Ma Junlin Xiong CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei 230026 China

This paper considers the stabilization problem for Markovian jump systems with time delays. Both the probability rate matrix and the state feedback control law are to be designed. A sufficient condition is established for such designs such that the resulting closed-loop Markovian jump system is stochastically stable. This condition is given in terms of a system of linear matrix inequalities with rank constraints, and can be solved using some existing algorithms. When the system has polytopic uncertainties, the robust stabilization problem is studied as well. Finally, a numerical example is given to show the validity of the proposed method.

关键词： Markovian jump systems time delay state feedback stabilization

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Encoding spatial Context for Large-Scale Partial-Duplicate Web Image Retrieval

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Journal of Computer Science & technology 2014年第5期29卷 837-848页

作者：周文罡李厚强卢亦娟田奇 Chinese Academy of Sciences Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Department of Electronic Engineering and Information Science University of Science and Technology of China Department of Computer Science Texas State UniversitySan Marcos TX 78666 U.S.A. MemberACMIEEE Department of Computer Science University of Texas at San AntonioSan Antonio TX 78249 U.S.A. Senior MemberIEEE

Many recent state-of-the-art image retrieval approaches are based on Bag-of-Visual-Words model and represent an image with a set of visual words by quantizing local SIFT（scale invariant feature transform） features. Feature quantization reduces the discriminative power of local features and unavoidably causes many false local matches between images, which degrades the retrieval accuracy. To filter those false matches, geometric context among visual words has been popularly explored for the verification of geometric consistency. However, existing studies with global or local geometric verification are either computationally expensive or achieve limited accuracy. To address this issue, in this paper, we focus on partialduplicate Web image retrieval, and propose a scheme to encode the spatial context for visual matching verification. An efficient affine enhancement scheme is proposed to refine the verification results. Experiments on partial-duplicate Web image search, using a database of one million images, demonstrate the effectiveness and efficiency of the proposed *** on a 10-million image database further reveals the scalability of our approach.

关键词： large-scale image retrieval spatial context coding spatial verification affine estimation

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A Novel Reconstruction Filter for the Azimuth Multi-Channel SAR

A Novel Reconstruction Filter for the Azimuth Multi-Channel ...

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2021 CIE International Conference on Radar, Radar 2021

作者： Shang, Mingyang Qiu, Xiaolan Yang, Junying Zhong, Lihua Xin, Yu Ding, Chibiao Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems Aerospace Information Research Institute Chinese Academic of Sciences Beijing China Beijing Institute of Remote Sensing Information Beijing China

ISBN: (纸本)9781665498142

The compensation of channel imbalances plays a vital role in signal processing of the azimuth multi-channel (AMC) synthetic aperture radar (SAR). In the operational AMC SAR system, the channel imbalance is usually considered to be a constant in both the time and frequency domain for one image. However, since the antenna patterns of all channels cannot be strictly consistent, the channel imbalances may vary with the Doppler frequency. This paper establishes the signal model of AMC SAR with channel imbalances changing with Doppler frequency and proposes a novel compensation method during the reconstruction for the echo signals received by all channels. This method has been verified by simulations and real data acquired by Gaofen-3 (GF-3). The result shows that this method has excellent potential for improving the image quality for AMC SAR. © 2021 IEEE.

关键词： Azimuth Multichannel SAR channel imbalances Doppler-varying GF-3 reconstruction

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L-hypersurface Based Parameters Selection for Sparse SAR Imaging via Composite Regularization

L-hypersurface Based Parameters Selection for Sparse SAR Ima...

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2022 International Conference on Radar systems, RADAR 2022

作者： Fan, Yizhe Xu, Zhongqiu Zhou, Guoru Zhang, Bingchen Wu, Yirong Aerospace Information Research Institute Chinese Academy of Sciences China Key Laboratory of Technology in Geo-spatial Information Processing and Application System School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781839537776

Composite regularization models are widely used in sparse signal processing, making multiple regularization parameters selection a significant problem to be solved. Variety kinds of composite regularization models are used in SAR imaging, including L1 and TV penalty, L1 and L2,1 penalty, etc. In this article, a new adaptive multiple regularization parameters selection method named L-hypersurface is proposed. The effectiveness of the proposed method is verified by experiments. Simulation experiments indicate that the selected optimal regularization parameters have satisfied reconstruction results, both visually and numerically. Furthermore, experiments on Gaofen-3 SAR satellite data are also exploited to show the performance of the proposed method. © IET Conference Proceedings. All rights reserved.

关键词： Synthetic aperture radar

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Parameter prediction method of SAR target simulation based on convolutional neural networks 12

Parameter prediction method of SAR target simulation based o...

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12th European Conference on Synthetic Aperture Radar, EUSAR 2018

作者： Shengren, Niu Xiaolan, Qiu Lingxiao, Peng Bin, Lei Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute ofElectronics Chinese Academy of Sciences University of Chinese Academy of Sciences China Suzhou Institute Institute of Electronics Chinese Academy of Sciences China

ISBN: (纸本)9783800746361

SAR image simulation plays a useful role in SAR target interpretation and recognition. The current SAR target simulation methods require high precision of models and simulation parameters, and are only forward processes which lack the feedback adjustment of real image. In this paper, a method of predicting simulation parameters from real images is proposed, which is based on convolutional neural networks(CNN). The architecture and the loss function of the CNN are modified to obtain better performance of the parameter inversion. From the simulation results, the predicted parameters improve the similarity between the simulation images and the real images. © VDE VERLAG GMBH Â Berlin Â Offenbach.

关键词： Synthetic aperture radar

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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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An automatic interpretation approach for high resolution urban remote sensing image using objects-based boosting model

An automatic interpretation approach for high resolution urb...

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2009 Joint Urban Remote Sensing Event

作者： Xian, Sun Hui, Long Hongqi, Wang Key Laboratory of Spatial Information Processing and Application System Technology Chinese Academy of Sciences Beijing China Institute of Electronics Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781424434619

For the purpose of interpreting urban remote sensing images more effectively and comprehensively, this paper proposes a new automatic approach using objects-based boosting model. The approach associates segmentation with recognition by constructing a hierarchical objects network at first, which effectively improves the problem of detecting targets with a modifiable sliding window existed in other methods. Then the probabilistic learning integrating multiple features including color, texture, shape and location is performed to train a multi-class classifier, and label all of the objects according to their classification values. The approach also applies spatial smoothing which incorporates contextual information to eliminate the adverse effects caused by background disturbance, occlusion and so on. After vectorization procedure, final result is given. Experiments demonstrate that proposed approach achieve high exactness and robustness in interpreting manifold urban remote sensing images. ©2009 IEEE.

关键词： Remote sensing

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The development and performance of the prototype coded ARC

The development and performance of the prototype coded ARC

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2009 Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2009

作者： Liang, Li Caiyun, Wang Yongtao, Zhu Jun, Hong Key Laboratory of Spatial Information Processing Application System Technology Chinese Academy of Sciences Beijing China Institute of Electronics Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781424427314

The development and performance of a prototype coded Active Radar Calibration (ARC) in S-band,which is used as a ground calibration reference standard for Synthetic Aperture Radar(SAR), are described. The coded ARC uses phase shifting and delay techniques which affect the SAR signal in azimuth and *** ratio of the ARC signal to the background signal is improved. An internal gain stabilization subsystem, which ensures the stability of the complete signal path and Effective Isotropic Radiated Power(ERIP),is included in the prototype coded ARC. An experiment was made in microwave chamber to prove the precision of the coded ARC's Radar Cross Section(RCS).The precision concluded from the result of the experiment is

关键词： Calibration

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