etam_boshuxingcheng

李国勇的最优控制理论，可以在其中学习到最优控制的思想(zui you kong zhi)

基于matlab的PCA人脸识别完成程序，包含特征值与特征向量的提取、训练样本以及最后的识别检验。(err)

随机减量技术（Random Decrement Technique-RDT），其主要是利用样本平均的方法，去掉响应中的随机成分，而获得初始激励下的自由响应，(Random decrement technique (Random Decrement Technique-RDT), whose main method is to use the sample mean, remove the random component of the response, and the response to the initial excitation freedom of)

Lagrange Interpolation

数据关联是多目标跟踪的一项关键技术。JPDA是大家公认的多目标跟踪中性能较好的数据关联算法，它 认为量测和目标是一一对应的关联关系，但在许多实际情况中，量测和目标是多一多对应的关系。针对上述情况，该文提 出了广义概率数据关联算法(Generalized Probability Data Association，GPDA)。文中从理论上对这两种算法的性能进行了 详细分析，并利用Monte Carlo技术对其性能进行了仿真比较。(Data association is one of the key technologies in multi—target tracking．And JPDA is considered as the best da· ta association method．JPDA considers the association of measurements with targets is simply one-to-one problem．But in many practical cases，the association of measurements with targets will be multiple—to—multiple problem．For this case，a Generalized Probability Data Association(GPDA)algorithm is proposed in this paper．Furthermore，this paper analyzes the performance of these two algorithms theoretically．And we give the comparative analysis of those performances by using Monte Carlo method．)

端正学习matlab的态度，教你怎么学习matlab (Matlab learning correct attitude to teach you how to learn matlab)

说明：  burg算法求功率谱,用matlab实现,其中用到了arburg和pburg等函数(burg algorithm for power spectrum, with the realization of matlab, which uses a function such as arburg and pburg)

This MATLAB Code plot the BER Vs Eb/No for M_ary FSK for differnt values of M. The user can choose the desired BER and the code will tell what is the required Eb/No to achieve this BER for differnt values of M. In addition, a plot of the shannon limit is available.

matlab入门书籍，对学习matlab有帮助(matlab entry books, learn matlab)

The fusion of high-spectral/low-spatial resolution multispectral and low-spectral/high-spatial resolution panchromatic satellite images is a very useful technique in various applications of remote sensing. Recently, some studies showed that a wavelet-based image fusion method provides high quality spectral content in fused images. However, most wavelet-based methods yield fused results with spatial resolution that is less than that obtained via the Brovey, IHS, and PCA fusion methods. In this paper, we introduce a new method based on a curvelet transform, which represents edges better than wavelets. Since edges play a fundamental role in image representation, one effective means to enhance spatial resolution is to enhance the edges. The curvelet-based image fusion method provides richer information in the spatial and spectral domains simultaneously. We performed Landsat ETM+ image fusion and found that the proposed method provides optimum fusion results.