Zongjie Cao

University of Electronic Science and Technology of China, Chengdu, Sichuan Sheng, China

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Publications (17)3.21 Total impact

  • Zongjie Cao, Lijia Chen
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    ABSTRACT: Because accurate identification cannot be obtained when the Identification Friend or Foe (IFF) sensor is employed separately, a radar sensor network (RSN) is designed to improve the identification capability in this paper. The content of this paper is focused on the information fusion algorithm, which is one of the key technologies in the RSN. The fuzzy c-means and the Bayesian network are chosen as the fusion algorithm. This algorithm can implement the identification friend or foe automatically after being trained by the training samples and expert's experience, and reduce the effect of uncertainties in the process of identification. At the same time, the algorithm can update the identification result with the augmentation of observations. The RSN can be expanded, if more information can be obtained, to adapt to the complicated environment, on the basis of this algorithm. The simulation results prove the validity and efficiency of the algorithm. Copyright © 2012 John Wiley & Sons, Ltd.
    Security and Communication Networks 06/2012; · 0.31 Impact Factor
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    ABSTRACT: The Compressive Sensing (CS) theory, which was first promulgated by Candes and Donoho in 2004, can break through the Nyquist Sampling Theorem to solve the bottleneck problems on practice such as the high-speed sampling and the mass-data storing. In this paper, the CS idea is applied to multi-baseline SAR tomography 3-D imaging. Aiming at solving the bad imaging quality problem caused by sparse baselines in practical applications, the sparse model in the height direction of multi-baseline SAR is built according to the limited scattering centers hypothesis. Thus the problem of imaging in the height direction is transformed to the problem of reconstructing a sparse signal. And the lp-norm minimization methods are utilized to solve this problem. Simulation results show that the proposed method not only can overcome the limitation of Rayleigh Criterion, but also can solve the problem brought about by the non-uniform distribution of baselines in noisy environment.
    Globecom Workshops (GC Wkshps), 2012 IEEE; 01/2012
  • Zongjie Cao, Zongyong Cui, Yong Fan, Qi Zhang
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    ABSTRACT: A hierarchical feature fusion strategy based on Support Vector Machine (SVM) and Dempster-Shafer Evidence Theory is proposed for SAR image automatic target recognition in this paper. This strategy has three fusion hierarchies corresponding to three features. Principle Component Analysis (PCA), Local Discriminant Embedding (LDE) and Non-negative Matrix Factor (NMF) features are extracted from images without preprocessing, and are fed to SVM classifier. However, not all features are used in each fusion process. At each fusion process, an empirical threshold T is used to determine the used features and hierarchy depth. Experiments on MSTAR public data set demonstrate that the proposed strategy outperforms the system combining the outputs of three features directly.
    Globecom Workshops (GC Wkshps), 2012 IEEE; 01/2012
  • Zongjie Cao, Jilan Feng, Rui Min, Yiming Pi
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    ABSTRACT: Feature extraction is a very important step in Synthetic Aperture Radar automatic target recognition (SAR ATR). In this paper, a feature extraction procedure based on the nonnegative matrix factorization (NMF) and Fisher linear discriminant (FLD) analysis is proposed for target recognition in SAR images. Firstly, segmented SAR images are processed by the NMF algorithm, which can extract nonnegative features that contain the local spatial structure information of targets. Then the FLD method is applied to the extracted features, thus the discriminability of the features can be enhanced. Both the spatial locality and separability between classes are enforced by this two-phase feature extracting procedure. Finally, the obtained features are used for automatic target recognition. Compared to several other methods, experimental results show the effectiveness of the proposed method for target feature extraction and recognition in SAR images.
    Communications (ICC), 2012 IEEE International Conference on; 01/2012
  • Rui Min, Yating Hu, Yiming Pi, Zongjie Cao
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    ABSTRACT: SAR Tomography Imaging Using Sparse Bayesian Learning
    IEICE Transactions. 01/2012; 95-B:354-357.
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    ABSTRACT: Feature extraction is the key technology and the core task of Synthetic Aperture Radar (SAR) target recognition. In this paper, a new target feature extracting method based on Sparse Non-negative Matrix Factorization (SNMF) is presented, which mainly use SNMF as the method to decompose the SAR target image and to construct the sparse feature vector. By this means, the similarity inside each cluster of the feature vectors is improved and the difference between the clusters is also raised. An identification test using the classification method of Support Vector Machine (SVM) demonstrates that the proposed method, compared to PCA, ICA and the general NMF feature extraction methods, can improve the stability and the accuracy of the target recognition significantly.
    Globecom Workshops (GC Wkshps), 2012 IEEE; 01/2012
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    ABSTRACT: This paper focus on the problems coming from uncertainty and target's time variation in the identification friend or foe, and solves them by using an algorithm combining fuzzy c-means(FCM) and dynamic Bayesian network(DBN). The simulations prove the algorithm's validity.
  • Jianping Xu, Yiming Pi, Zongjie Cao
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    ABSTRACT: UWB linear frequency modulated (LFM) signals are widely used in radar, sonar and communication systems. In some applications, the detection of LFM signals and estimation of time-delay are very important. It needs very high sampling rate to address the problems for UWB LFM signal under Nyquist sampling theory which exceeds the current ADC capacity. In this paper, we propose a Compressive Sensing (CS) based method to solve the problem with ultra low sampling rate. We adopt an FrFt based sparse dictionary for CS because of the energy concentration property of LFM signal in the fractional Fourier domain. The performance is much better than the already existed method which used signal-matched sparse dictionary in noise condition. Experiments based on simulated data are carried out to testify the results.
    Signal Processing (ICSP), 2010 IEEE 10th International Conference on; 11/2010
  • Jilan Feng, Zongjie Cao, Yiming Pi
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    ABSTRACT: A variational level set approach based on the G0 distribution is proposed for SAR image segmentation problem. By introducing G0 distribution, the proposed method is more applicable for SAR image segmentation. To implement the segmentation, the parameters of the G0 distribution are estimated by the method based on Mellin transform, and level set function is applied for the numerical solution of PDEs which are derived from the minimization of the energy function. The experiments based on both synthetic and real SAR images prove that the proposed algorithm is applicable for a series of SAR images with different scenes.
    Synthetic Aperture Radar (EUSAR), 2010 8th European Conference on; 07/2010
  • Source
    Jianping Xu, Yiming Pi, Zongjie Cao
    EURASIP J. Adv. Sig. Proc. 01/2010; 2010.
  • HongXia Zhang, ZongJie Cao, YiMing Pi
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    ABSTRACT: Most of the existing algorithms for multi-sensor tracks fusion are based on sensors with the same sampling rate, ignoring communication delay and different initial time. In this paper, a method to fuse information of asynchronous sensor tracks is proposed. At the track fusion center, position and velocity information of two sensors with different sampling rates and communication delays are fused. The fused track is an asynchronous track, which has minimum error covariance. This proposed fusion method not only avoids the time correction of asynchronous data, but also improves the tracking performance of multiple sensors. The performance of the proposed algorithm is also studied.
  • Jinfeng Wang, Yiming Pi, Zongjie Cao
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    ABSTRACT: In this letter, a novel approach for the coregistration of synthetic aperture radar (SAR) images is proposed based on the level set method. The features of regions are detected by segmenting the images, and the images are coregistered by matching the detected features. The energy functional of level sets is formulated with respect to detecting and matching features. The coregistration is achieved by minimizing the energy functional. Compared with the conventional tie-patch method, the results on a series of simulated experiments and real SAR data demonstrate the feasibility of the proposed approach.
    IEEE Geoscience and Remote Sensing Letters 11/2008; · 1.82 Impact Factor
  • Source
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    ABSTRACT: SAR image automatic segmentation is a hard work due to the presence of speckle noise. In this paper, a variational level set approach for SAR image is presented. A new energy functional is defined by taking account of a statistical model of speckle noise. The energy functional is with respect to level set function, which is obviously different from the energy functional with respect to parameterized curve in general level set approach. Segmentation is implemented by minimizing the energy formulation via level set approach. The performance of the approach is verified by MSTAR SAR images. It shows that the energy well describes the property of SAR image, thus accurately and automatically extracts the regions of interest in SAR image but without any speckle pre-processing step.
    Synthetic Aperture Radar (EUSAR), 2008 7th European Conference on; 07/2008
  • Yusheng Fu, Zongjie Cao, Yiming Pi
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    ABSTRACT: In this letter, a multiphase level set approach unifying region and boundary-based information for multi-region segmentation of Synthetic Aperture Radar (SAR) image is presented. An energy functional that is applicable for SAR image segmentation is defined. It consists of two terms describing the local statistic characteristics and the gradient characteristics of SAR image respectively. A multiphase level set model that explicitly describes the different regions in one image is proposed. The purpose of such a multiphase model is not only to simplify the way of denoting multi-region by level set but also to guarantee the accuracy of segmentation. According to the presented multiphase model, the curve evolution equations with respect to edge curves are deduced. The multi-region segmentation is implemented by the numeric solution of the partial differential equations. The performance of the approach is verified by both simulation and real SAR images. The experiments show that the proposed algorithm reduces the speckle effect on segmentation and increases the boundary alignment accuracy, thus correctly divides the multi-region SAR image into different homogenous regions.
    Journal of Electronics (China) 06/2008; 25(4):556-561.
  • Haijiang Wang, Yiming Pi, Zongjie Cao
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    ABSTRACT: Polarimetric SAR image classification is an important research area. Various classification methods continue to be developed for specific applications. In this paper, A new unsupervised classification method for polarimetric SAR images is proposed. It is based on independent component analysis (ICA). By ICA processing, several independent components are extracted from the channels of the SAR images. One of the independents is regarded as speckle noise and thrown away. By taking each remained independent as a kind of target, a classified SAR image with higher classification accuracy can be obtained.
    Natural Computation, 2007. ICNC 2007. Third International Conference on; 09/2007
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    Zongjie Cao, Ying Tan, Jilan Feng
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    ABSTRACT: This paper presents a variational model based segmentation approach for polarimetric synthetic aperture radar (PolSAR) images. The formulation for PolSAR image segmentation is based on a scaled Wishart distribution based continuous Potts model, which can partition the image domain into distinct regions with respect to the statistical property of PolSAR data. To make the segmentation efficient, a duality based optimization approach is utilized to minimize the energy functional. Moreover, an automatic initialization approach which takes the unsupervised H–a classification result of the polarimetric data as input is used to initialize the segmentation process. This approach can estimate the appropriate number of clusters and the corresponding classification map for the PolSAR data, which are used as the input of the following variational segmentation approach. In such a way, the proposed approach is carried out in a fully unsupervised way. Both of the polarimetric decomposition features and the statistical characteristics are used to get the final segmentation result, which helps to increase the accuracy. Experimental results demonstrate the effectiveness of the proposed approach. Without any artificial supervision, the proposed approach can produce superior segmentation results than results obtained with random initialized variational approach and Wishart–H–a classification approach.
    EURASIP Journal on Wireless Communications and Networking 2013(1). · 0.54 Impact Factor
  • Source
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    ABSTRACT: Synthetic aperture radar (SAR) automatic target recognition (ATR) is playing a very important role in military and civil field. Much work has been done to improve the performance of SAR ATR systems. It is well-known that ensemble methods can be used for improving prediction performance. Thus recognition using multiple classifiers fusion (MCF) has become a research hotspot in SAR ATR. Most current researchers focus on the fusion methods by parallel structure. However, such parallel structure has some disadvantages, such as large time consumption, features attribution conflict and low capability on confuser recognition. A hierarchical propelled strategy for multi-classifier fusion (HPSMCF) is proposed in this paper. The proposed HPSMCF has the characters both of series and parallel structure. Features can be used more effective and the recognition efficiency can be improved by extracting features and fusing the probabilistic outputs in a hierarchical propelled way. Meanwhile, the confuser recognition can be achieved by setting thresholds for the confidence in each level. Experiments on MSTAR public data demonstrate that the proposed HPSMCF is robust for variant recognition conditions. Compared with the parallel structure, HPSMCF has better performance both on time consumption and recognition rate.
    EURASIP Journal on Wireless Communications and Networking 2013(1). · 0.54 Impact Factor

Publication Stats

6 Citations
3.21 Total Impact Points


  • 2007–2010
    • University of Electronic Science and Technology of China
      • School of Electronic Engineering
      Chengdu, Sichuan Sheng, China
  • 2008
    • Sichuan University
      • School of Electronics and Information Engineering
      Hua-yang, Sichuan, China