Wenxian Yu

Shanghai Jiao Tong University, Shanghai, Shanghai Shi, China

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Publications (28)20.59 Total impact

  • Min Tang, Xia Chen, Weidong Hu, Wenxian Yu
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    ABSTRACT: This study considers probabilistic fuzzy systems constructed using Mamdani probabilistic fuzzy rules. As a generalisation of deterministic fuzzy systems, Mamdani probabilistic fuzzy systems better model practical complex systems involving uncertainty because they combine the interpretability of fuzzy systems with the statistical properties of probabilistic systems. Using probabilistic fuzzy rules, both probabilistic uncertainty and linguistic ambiguity are handled simultaneously with a single framework. Considering that the information available often consists of a training set of input–output data pairs, a general method for generating Mamdani probabilistic fuzzy rule bases from numerical data pairs is presented. A fuzzy reasoning method is used on the generated probabilistic fuzzy rule base to derive a map leading from the input space to the output space, and a probabilistic fuzzy system is constructed. We use this probabilistic fuzzy modelling method for nonlinear regression analysis. The effectiveness of the proposed method is demonstrated by a comparison with similar regression techniques.
    Information Sciences 12/2012; 217:21–30. DOI:10.1016/j.ins.2012.06.021 · 3.89 Impact Factor
  • Min Tang, Xia Chen, Weidong Hu, Wenxian Yu
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    ABSTRACT: The design of type-2 fuzzy rule-based classification systems from labeled data is considered in this study. With the aid of interval type-2 fuzzy sets, which can effectively capture uncertainties in the data, a compact and interpretable interval type-2 fuzzy rule base with fewer rules is constructed. Corresponding type-2 fuzzy reasoning method for classification is also presented. The validity of this classification system is shown through experimental results on several data sets.
    Integrated Uncertainty in Knowledge Modelling and Decision Making - International Symposium, IUKM 2011, Hangzhou, China, October 28-30, 2011. Proceedings; 01/2011
  • Yutao Zhu, Yi Su, Wenxian Yu
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    ABSTRACT: With the inverse synthetic aperture radar (ISAR) imaging model, targets should move smoothly during the coherent processing interval (CPI). Since the CPI is quite long, fluctuations of a target's velocity and gesture will deteriorate image quality. This paper presents a multiple-input-multiple-output (MIMO)-ISAR imaging method by combining MIMO techniques and ISAR imaging theory. By using a special M-transmitter N-receiver linear array, a group of M orthogonal phase-code modulation signals with identical bandwidth and center frequency is transmitted. With a matched filter set, every target response corresponding to the orthogonal signals can be isolated at each receiving channel, and range compression is completed simultaneously. Based on phase center approximation theory, the minimum entropy criterion is used to rearrange the echo data after the target's velocity has been estimated, and then, the azimuth imaging will finally finish. The analysis of imaging and simulation results show that the minimum CPI of the MIMO-ISAR imaging method is 1/MN of the conventional ISAR imaging method under the same azimuth-resolution condition. It means that most flying targets can satisfy the condition that targets should move smoothly during CPI; therefore, the applicability and the quality of ISAR imaging will be improved.
    IEEE Transactions on Geoscience and Remote Sensing 08/2010; 48:3290-3299. DOI:10.1109/TGRS.2010.2045230 · 2.93 Impact Factor
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    ABSTRACT: The array design of radar antenna is no more a new problem at all. Nevertheless, the array design of multi-channel radar, especially that of MIMO (multiple-input multiple-output) radar, is a new problem. The performance of space diversion and spatial sampling of target scattering are dependent on the array design of MIMO radar, and consequently, which in turn will determine the performances of target parameter estimation, DOA, and imaging. In this paper, commencing with target scattering model, T/R signal model of MIMO radar, and spatial sampling ability for this system are analyzed. Conceptually, under the far field condition, spatial convolution principle determines the array design theory. Based on this idea, array design method and also algorithm are proposed in this paper.
    Science in China Series F Information Sciences 07/2010; 53(7):1470-1480. DOI:10.1007/s11432-010-4013-x · 0.66 Impact Factor
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    ABSTRACT: This paper presents an efficient algorithm combining the stabilized biconjugate gradient fast Fourier transform (BCGS-FFT) method with an improved discrete complex image method (DCIM) for electromagnetic scattering from electrically large objects in both lossless and lossy multilayered media. The required spatial Green's functions obtained by the improved DCIM are accurate both in the near- and far-field regions without any quasi-static and surface-wave extraction. Then, the scattering by buried objects is considered using the BCGS-FFT method combined with the improved DCIM. Numerical results show the improved DCIM can save tremendous CPU time in scattering involving buried objects.
    IEEE Transactions on Geoscience and Remote Sensing 03/2010; 48:1180-1185. DOI:10.1109/TGRS.2009.2031106 · 2.93 Impact Factor
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    ABSTRACT: An efficient particle filter based distributed track-before-detect (PF-DTBD) algorithm is presented in this paper. It key idea is the fusion of multi-sensor local estimated conditional probability density functions (PDFs). Firstly, the PDFs among sensors nodes are estimated by multivariate kernel density estimation (MKDE) technique based on finite particles set and fused to calculate the fused particle's weight at fusion node. Next, according to Bayes rule, we prove that the unnormalized fused particle' weight is actually composed of sensors' local measurement likelihood, which makes the likelihood ratio test feasible at fusion node. Finally we introduce a detection scheme combining sequential probability ratio test (SPRT) and fixed sample size (FSS) likelihood ratio test to definitely realize TBD process for weak targets. Simulation results show our algorithm is efficient, which reduces delay of detection and improves the precision of state estimation simultaneously.
    Radar Conference, 2009 IET International; 05/2009
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    ABSTRACT: In this paper, an ensemble of neural networks based incremental learning algorithm with weights updated voting is described. The algorithm defines the class kernel function of the training database of the component neural network in the ensemble. The voting weights are updated based on the distance between the test instance and the kernel function. This method can adaptively update the voting weights according to the classification performance of the component neural network on the test pattern and it is more optimal than the stable weights voting strategy. Experimental results show that the ensemble of neural networks based incremental learning algorithm with weights updated voting is more promising than that with stable weights voting rule.
    Advances in Neural Networks - ISNN 2009, 6th International Symposium on Neural Networks, ISNN 2009, Wuhan, China, May 26-29, 2009, Proceedings, Part I; 01/2009
  • Yonghui Wu, Kefeng Ji, Wenxian Yu, Yi Su
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    ABSTRACT: The scattering measurements of individual pixels in polarimetric SAR images are affected by speckle; hence, the performance of classification approaches, taking individual pixels as elements, would be damaged. By introducing the spatial relation between adjacent pixels, a novel classification method, taking regions as elements, is proposed using a Markov random field (MRF). In this method, an image is oversegmented into a large amount of rectangular regions first. Then, to use fully the statistical a priori knowledge of the data and the spatial relation of neighboring pixels, a Wishart MRF model, combining the Wishart distribution with the MRF, is proposed, and an iterative conditional mode algorithm is adopted to adjust oversegmentation results so that the shapes of all regions match the ground truth better. Finally, a Wishart-based maximum likelihood, based on regions, is used to obtain a classification map. Real polarimetric images are used in experiments. Compared with the other three frequently used methods, higher accuracy is observed, and classification maps are in better agreement with the initial ground maps, using the proposed method.
    IEEE Geoscience and Remote Sensing Letters 11/2008; DOI:10.1109/LGRS.2008.2002263 · 1.81 Impact Factor
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    ABSTRACT: Geophysical model function is the basis for the wind vector retrieval with scatterometer and a number of models have been developed to operationally retrieve the ocean surface wind in the past three decades. However, none of the operational models ever took the water surface temperature into account in its modeling, which is considered to have some effect on the ocean backscattering, and in turn on the model accuracy. Taking Sea Winds as an example, this paper attempts to develop new geophysical model functions with surface temperature to be taken into account by using its level 2A data and corresponding buoy data. For contrast, two independent models are established for the ocean water and fresh water respectively. The modeling results and analysis indicate that some effect of the surface temperature on backscatter were found for both types of water, but with a larger extent of the temperature effect for fresh water.
    Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE International; 08/2008
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    ABSTRACT: Geophysical model function (GMF) is the basis and prerequisite for the ocean surface wind vector retrieval with scatterometers. Among many operational models, the Qscat-1 model was specifically developed for SeaWinds scatterometer and is being applied to its operational wind retrieval. This paper is to validate the accuracy of the Qscat-1 model by using some SeaWinds Level 2 data and corresponding buoy data. First, a comparison between L2B and co-located buoy wind speed was made to analyze the systematic bias between them, and then a new geophysical model function was established using the match-ups of the L2A and buoy data to further valuate the accuracy of the Qscat-1 model. The analytical and modeling results indicate that there may be some systematic error in the Qscat-1 model.
    Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE International; 08/2008
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    ABSTRACT: Group targets data association is a key part of group targets analysis through multisensor information fusion. Traditional data association methods using group centroids are no longer applicable when the observation areas of multiple sensors partially overlap. Aiming at the problem a new group targets data association algorithm is proposed in this paper. Firstly, the local structures centered on every point within the point pattern which represents the group target data are constructed based on its Delaunay graph. A pair of reference subsets is found from two point patterns to be associated via local structure matching. Then the two point patterns are aligned by the transformation parameter estimated using the reference subsets. Finally the various group targets data are associated once the aligned data have been matched. Simulation experiments show that the proposed algorithm is an effective solution for the group targets data association problem of multiple sensors with their observation areas partially overlapped.
    Information and Automation, 2008. ICIA 2008. International Conference on; 07/2008
  • Zenghui Zhang, Weidong Hu, Wenxian Yu
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    ABSTRACT: This paper introduces the preconditioned methods for Space-Time Adaptive Processing (STAP). Using the Block-Toeplitz-Toeplitz-Block (BTTB) structure of the clutter-plus-noise covariance matrix, a Block-Circulant-Circulant-Block (BCCB) preconditioner is constructed. Based on the preconditioner, a Preconditioned Multistage Wiener Filter (PMWF) which can be implemented by the Preconditioned Conjugate Gradient (PCG) method is proposed. Simulation results show that the PMWF has faster convergence rate and lower processing rank compared with the MWF.
    Journal of Electronics (China) 07/2008; 25(4):465-470. DOI:10.1007/s11767-006-6249-6
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    ABSTRACT: Abstract—Discrete complex image method,is introduced to get a closed-form dyadic Green’s function by a sum,of spherical waves. However, the simulation result by the traditional discrete complex image method is only valid in near-field for several wavelengths. In this paper, we analyze the form of spectral domain dyadic Green’s function in the whole kρ plane and the variety of valid range of simulation results by different sampling paths in two-level discrete complex image method. Consequently, for dyadic Green’s function, surface wave pole contribution both in spectral domain and spatial domain is clarified. We introduce the automatic incorporation of surface wave poles in discrete complex image method without extracting surface wave poles. The contribution of surface wave poles in spectral domain and spatial domain,dyadic Green’s function is further confirmed in the new 162,Zhuang et al. method. Besides, this method can represent dyadic Green’s function by spherical waves in the layer where the source and field points are. So it satisfies the splitting requirement and consequently reduces
    Progress In Electromagnetics Research 01/2008; 80:161-178. DOI:10.2528/PIER07110105 · 5.30 Impact Factor
  • Yonghui Wu, Kefeng Ji, Yu Li, Wenxian Yu, Yi Su
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    ABSTRACT: Several frequently used feature vectors and segmentation methods are investigated, and a novel method is proposed for segmenting fully polarimetric SAR images by starting from the statistical characteristic and the interaction between adjacent pixels. In order to use fully the statistical a priori knowledge of the data and the spatial relation of neighboring pixels, Wishart distribution is integrated with Markov random field (MRF), and then an iterative conditional modes (ICM) algorithm is used to implement a maximum a posteriori (MAP) estimation of pixel labels. Although ICM has good robustness and fast convergence rate, it is affected easily by initial conditions, so a Wishart-based ML is used to obtain the initial segmentation map, with the statistical a priori knowledge also exploited completely in the initial segmentation step. Using fully polarimetric SAR data, acquired by the NASA/JPL AIRSAR sensor, the new approach is compared with several frequently used methods. Better segmentation performance, as well as better connectivity, less isolated pixels and small regions, are observed.
    Synthetic Aperture Radar, 2007. APSAR 2007. 1st Asian and Pacific Conference on; 12/2007
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    ABSTRACT: Feature extraction and selection play an important role in radar target recognition. This paper focuses on evaluating feature separability for SAR ATR and selecting the best subset of features. In details, fifteen features extracted from T72, BTR70 and BMP2 in MSTAR standard public dataset are examined, which are divided into seven categories: standard deviation, fractal dimension, weighted-rank fill ratio, size-related features, contrast-based features, count feature, projection feature, and moment features. Since the number of samples is small, a new separability criterion based on the overlap degree of each two class regions is proposed to assess the separability of these features. Here the class region is described by support vector data description (SVDD) method for good generalization. Based on the proposed criterion, a forward feature selection method is adopted to choose the best subset of features. Because of the strong variability of the feature against aspect, the features are analyzed under different aspect sectors within 360°angle range stepped by 15°, 30 °, and 60°, respectively. Experiments using MSTAR dataset validate the criterion, and the best subset of features is determined.
    Proceedings of SPIE - The International Society for Optical Engineering 11/2007; DOI:10.1117/12.774834 · 0.20 Impact Factor
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    ABSTRACT: Different from the airborne scenario, space time adaptive processing (STAP) for space based radar (SBR) is a full system design task whose performance is significantly determined by radar system parameters such as array aspect ratio and PRF. To mitigate clutter, the two parameters should be appropriately designed. The suppression of mainbeam clutter is discussed while taking the earth's rotation and high range foldovers into consideration. Clutter rank is derived by modeling it as an equivalent two-dimensional spatially band-limited process and it is shown to be a function of array aspect ratio and radar PRF. Relationships between clutter rank and the two parameters are analyzed. Optimal array aspect ratio and radar PRF are obtained by minimizing clutter rank with the tradeoff of minimum discernable velocity (MDV). A high fidelity modeling of SBR is given to illustrate the effectiveness of the method.
    Proceedings of SPIE - The International Society for Optical Engineering 11/2007; DOI:10.1117/12.774578 · 0.20 Impact Factor
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    ABSTRACT: A method of ship formation recognition is proposed based on the information fusion of spaceborne IMINT (image intelligence) and ELINT (electronic intelligence) in this paper. Firstly, the composition and the battle array of the observed ship formation are derived from the spaceborne IMINT information of each individual ship. The beliefs about the composition and the battle array of the observed formation are calculated separately. A synthetic evaluation method is used to get the BPAF (basic probability assignment function) from IMINT information using the aforementioned two beliefs. Secondly, by computing the matching measure between the observed emitter set and that of the typical formation entity, the BPAF based on ELINT information is obtained. Thirdly, the two BPAFs based on the information of spaceborne IMINT and ELINT are combined with DS (Dempster-Shafer) evidence theory. Finally, a decision is made according to the combined BPAF. The experiment indicates that our proposed methods to obtain the BPAFs are practicable and the formation recognition accuracy is greatly improved compared to the results which use only one of the two sources.
    Proceedings of SPIE - The International Society for Optical Engineering 11/2007; DOI:10.1117/12.775016 · 0.20 Impact Factor
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    ABSTRACT: Estimating debris population is of great significance for assessing impact risk to spacecrafts, predicting the long-term growth potential and validating space debris models. A method of estimating the confidence interval for debris population using radar beam fence is proposed. The problem whether a debris object that may cross the beam fence, i.e. has large enough inclination and radar cross section, is actually detected is modeled as a (0-1) distribution. Using orbital altitudes measured, the average probability of crossing the beam fence is obtained. The confidence interval for debris population is estimated by the Central Limitation Theorem. Experimental results show the validity of the method.
    Proceedings of SPIE - The International Society for Optical Engineering 11/2007; DOI:10.1117/12.774787 · 0.20 Impact Factor
  • Yonghui Wu, Kefeng Ji, Wenxian Yu, Yi Su
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    ABSTRACT: Information contained in fully polarimetric SAR data is plentiful. How to exploit the information to improve accuracy is important in segmentation of fully polarimetric SAR images. Several frequently used feature vectors and methods are investigated, and a novel method is proposed for segmenting multi-look fully polarimetric SAR images in this paper, starting from the statistical characteristic and the interaction between adjacent pixels. In order to use fully the statistical a priori knowledge of the data and the spatial relation of neighboring pixels, the Wishart distribution of the covariance matrix is integrated with the Markov random field, then the iterated conditional modes (ICM) is taken to implement the maximum a posteriori estimation of pixel labels. Although the ICM has good robustness and fast convergence, it is affected easily by initial conditions, so the Wishart-based ML is used to obtain the initial segmentation map, in order to exploit completely the statistical a priori knowledge in the initial segmentation step. Using multi-look fully polarimetric SAR images, acquired by the NASA/JPL AIRSAR sensor, the new approach is compared with several other commonly used ones, better segmentation performance and higher accuracy are observed.© (2007) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
  • Yonghui Wu, Kefeng Ji, Wenxian Yu, Yi Su
    [Show abstract] [Hide abstract]
    ABSTRACT: Information contained in fully polarimetric SAR data is plentiful. How to exploit the information to improve accuracy is important in segmentation of fully polarimetric SAR images. Several frequently used feature vectors and methods are investigated, and a novel method is proposed for segmenting multi-look fully polarimetric SAR images in this paper, starting from the statistical characteristic and the interaction between adjacent pixels. In order to use fully the statistical a priori knowledge of the data and the spatial relation of neighboring pixels, the Wishart distribution of the covariance matrix is integrated with the Markov random field, then the iterated conditional modes (ICM) is taken to implement the maximum a posteriori estimation of pixel labels. Although the ICM has good robustness and fast convergence, it is affected easily by initial conditions, so the Wishart-based ML is used to obtain the initial segmentation map, in order to exploit completely the statistical a priori knowledge in the initial segmentation step. Using multi-look fully polarimetric SAR images, acquired by the NASA/JPL AIRSAR sensor, the new approach is compared with several other commonly used ones, better segmentation performance and higher accuracy are observed.
    Proceedings of SPIE - The International Society for Optical Engineering 10/2007; DOI:10.1117/12.736395 · 0.20 Impact Factor

Publication Stats

86 Citations
20.59 Total Impact Points

Institutions

  • 2010–2012
    • Shanghai Jiao Tong University
      • • Department of Electronic Engineering
      • • School of Electronic, Information and Electrical Engineering
      Shanghai, Shanghai Shi, China
  • 2011
    • Shanghai University
      • Department of Electronic and Information Engineering
      Shanghai, Shanghai Shi, China
  • 2005–2010
    • National University of Defense Technology
      • • College of Electronic Science and Engineering
      • • National Key Laboratory of Automatic Target Recognition
      Ch’ang-sha-shih, Hunan, China
  • 2007–2008
    • Changsha University of Science and Technology
      Ch’ang-sha-shih, Hunan, China
    • National Defense University
      Washington, Washington, D.C., United States