Dongping Liao

National University of Defense Technology, Changsha, Hunan, China

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Publications (5)0 Total impact

  • Meimei Fan, Dongping Liao, Xiaofeng Ding, Xiang Li
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    ABSTRACT: The optimal waveform for extended target recognition is directly affected by the target impulse response, which is sensitive to the target aspect. Hence, the variation of target aspect needs to be considered when the target is moving. Aiming at this problem, a new framework of cognitive radar is proposed. It predicts the new aspect via least square support vector machines (LSSVM) by using the prior knowledge of target aspect, and then obtains the optimal waveform based on not only the updated prior probabilities of the target hypothesis but also the updated TIR in a circular of interrogation. Simulations part shows the loss of recognition efficiency for a moving target when treated as static by the method in previous literature, and proves the validity of the proposed method.
    01/2012;
  • Meimei Fan, Dongping Liao, Xiaofeng Ding, Xiang Li
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    ABSTRACT: The existence of clutter in received waveform makes the waveform design a difficult problem. For the clutter is correlated with the transmitted signal, it comes to an iterative method in the traditional ways and is difficult to achieve an analytical solution. This paper presents a waveform design method in clutter for target recognition. It transforms the target recognition problem in complex WSS Gaussian clutter into a hypothesis test procedure with mean shift Gaussian-Gaussian probability distribution function. The optimum waveform is the one that maximizes the shift distance and the analytical solution can be obtained. The simulation results show that the recognition performance improves a lot when compared to the LFM signal with equal energy and bandwidth.
    01/2011;
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    ABSTRACT: Target recognition performance can be affected by radar waveform parameters. In this paper, we established rigorous relationship between target recognition efficiency and the parameters of a repeatedly transmitted waveform. It is based on Kullback-Leibler Information Number of single observation (KLINs), which measures the dissimilarity between targets depicted by a range-velocity double spread density function in frequency domain. We considered two signal models which are different in the coherence of the observations. The method we proposed takes advantage of the methodology of sequential hypothesis test, and then the recognition performance in terms of correct classification rate is expressed by Receiver Operating Characteristic (ROC). Simulation results about the parameters of LFM signal show the validity of the method. Key wordsTarget recognition performance–Radar waveform parameter–Kullback-Leibler Information Number (KLIN)
    Journal of Electronics (China) 01/2011; 28(1):77-86.
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    ABSTRACT: In allusion to the shortcoming of progressive transductive support vector machine learning algorithm, a fast algorithm is presented. Using region labeling rule to substitute the former’s pairwise labeling rule, the algorithm inherits its rules of progressive labeling and dynamic adjusting, and raises its speed at the same time. Experiments with radar raw data show the validity of this algorithm.
    Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics 01/2007; 29(1).
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    ABSTRACT: The training of a support vector machine is difficult when classifying a large-scale data set. Pre-extracting a support vector for training a support vector machine is one of the solutions to this difficulty, but the choice is very hard. A method based on density is proposed to pre-extract a support vector. This method needs not to be confirmed whether the training examples are linear separable, and has a strong ability of diminishing the effect of noises and outliers. The method is simple and easy to realize. Experiments show its validity.
    Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics 01/2007; 29(11).