MATLAB_directions

使用多种算法（DAC、Hyperbolic、DAC、SI、SX、Taylar）实现多传感器对单点定位（采用高斯噪声），其中Hyperbolic与SI为3d定位，其他的为平面定位(Using a variety of algorithms (DAC, Hyperbolic, DAC, SI, SX, Taylar) to achieve single point positioning (using Gauss noise), Hyperbolic and SI are 3D location, and the other is plane location)

source for super-resolution method (PG, POCS, IBP) using Matlab

局部频谱细化算法的实时效率 比较zoom-fft各种算法(Local spectrum thinning algorithm more efficient, real-time zoom-fft algorithms)

上传一个判断传递函数的稳定性的程序，当传函零极点位于虚轴右侧时系统不稳定，输出不稳定提示，系统稳定时输出稳定系统的传函。(Upload a function to determine the stability of the program transfer, the transfer function when the right side of the imaginary axis pole-zero when the system is unstable, unstable output prompt, the output transfer function to stabilize the system when the system is stable.)

matlab的常用指针表，综合辅导与指南，符号语言工具箱(matlab common pointer table, comprehensive counseling and guidance, sign language toolbox)

擷取下來的指紋, 周邊環境雜訊太多, 可使用分割步驟, 先將又笑的指紋分割下來(Fingerprint capture down, too much noise surrounding environment, use separate steps, first laugh of the fingerprint split down)

basic function that you need for radar systems

图像超分辨率重建算法，通过稀疏混合估计来实现，效果不错。(Image super-resolution reconstruction algorithm, by mixing estimates sparse achieved good results.)

svm分类的源代码例子，很基础的，适合初学者，一看就懂(svm the classification source code examples, very basic, for beginners to understand at a glance)

K最邻近密度估计技术是一种分类方法，不是聚类方法。 不是最优方法，实践中比较流行。 通俗但不一定易懂的规则是： 1.计算待分类数据和不同类中每一个数据的距离（欧氏或马氏）。 2.选出最小的前K数据个距离，这里用到选择排序法。 3.对比这前K个距离，找出K个数据中包含最多的是那个类的数据，即为待分类数据所在的类。(K nearest neighbor density estimation is a classification method, not a clustering method. It is not the best method, but it is popular in practice. Popular but not necessarily understandable rule is: 1. calculate the distance between the data to be classified and the data in each other (Euclidean or Markov). 2. select the minimum distance from the previous K data, where the choice sorting method is used. 3. compare the previous K distances to find out which K data contains the most data of that class, that is, the class to which the data to be classified is located.)