berreleighchannel

collection of Indonesian matter for high school

matlab入门教程 简单易学,特别适合初学者!!1()

改进的非相干信号子空间宽带测向算法(最大功率频点选择)(Improved non-coherent signal subspace wideband direction finding algorithm (maximum power frequency selection))

本书附带光盘中包括了全书所有实例对应的MATLAB的M文件。所有代码按照章节存放在各个文件夹下，如“第4章 插值”文件夹下存放了本书第4章所有的算法程序代码，对于算法的程序代码，在光盘中存为同函数名的M文件，例如，Language.m表示求已知数据点的拉格朗日插值多项式的程序，依此类推。(Book with CD-ROM includes all instances of the book corresponds to the MATLAB M-file. All code is stored in accordance with chapters in every folder, such as " Chapter 4 interpolation" folder stored under chapter 4 of the algorithm all the code for the algorithm of the program code, stored on CD-ROM for the same function name in the M file, for example, Language.m known data points that seek Lagrange polynomial interpolation procedures, and so on.)

基于capon的谱估计，关于DOA的算法，可用(Capon based spectral estimation algorithm on DOA, the available)

ldcp编码解码源程序，用c语言写的，最后生成mex文件用于matlab使用 (ldcp codec source code, using c language, and finally generate mex file for matlab use)

本例程是运用人工神经网络在电网经济调度中的智能算法(This routine is the use of artificial neural networks in the economic dispatch of intelligent algorithms)

在TDOA定位系统中，运用平滑的的IMM算法对机动目标进行被动跟踪 (TDOA positioning system, the use of smooth IMM algorithm for passive tracking of maneuvering targets)

一个身体部位比较模型的新方法网络（BAN）的，可以容纳多个环节和多个科目。所述的绝对测量允许跨频谱可能刻画的比较 从单参数为整个合奏，通过基于参数化到每个活动，每个学科和每个环节模型。使用错误，并明确之间权衡复杂性，在一个善良的适应措施相结合，显示有重要的影响时，适用于一系列典型的禁止通道数据。它是有不同的 在模式的选择的影响，以及它相关的复杂性，混合活动的“日常”的数据，设置活动相比，动态数据（例如步行）。平均路径损耗的不足，甚至位数的路径损失的措施，作为唯一的表征还强调“禁止通道。 (A new approach to compare models for body area networks (BAN) that accommodates multiple links and multiple subjects is presented. The absolute measure described allows comparison across a spectrum of possible characterizations ranging from single-parameter for an entire ensemble, through to per-activity, per-subject and per-link based parameterized models. The use of an explicit trade-off between error and complexity, combined in a goodness-of-fit measure, is shown to have important consequences when applied to a range of typical BAN channel data. It is shown that there are different implications in choice of model, and it’s associated complexity, for mixed-activity “everyday” data, when compared with set-activity dynamic data (e.g. walking). The deficiency of mean path loss, or even median path loss measures, as a sole characterization of the BAN channel is also highlighted.)

卡尔曼滤波器的操作包括两个阶段:预测与更新。在预测阶段,滤波器使用上一状态的估计,做出对当前状态的估计。在更新阶段,滤波器利用对当前状态的观测值优化。(Kalman filter operation consists of two stages: prediction and update. In the prediction stage, the filter is estimated using the previous state, to make an estimate of the current state. In the update phase, the filter use observations on the current state of the optimization.)