target-tracking-using-kalman

这个程序能实现在杂波环境下对多目标的跟踪。(This program can realize multi-target tracking in clutter environment with JPDA algorithm.)

一个用来计算高维数据线性图形嵌入的matlab函数，是几乎所有线性降维算法（如LPP, NPE, IsoProjection, LSDA.）都要用的一个文件，在这里上传给大家，一定用得着！(A high dimensional data used to calculate the linear graphics embedded matlab function, is that almost all linear dimensionality reduction algorithms (such as LPP, NPE, IsoProjection, LSDA.) Should be used in a document From here to the U.S., we use gained!)

用MATLab仿真了基于矩量法的V形阵子天线的方向图和天线上的电流分布图(With MATLab simulation time around based on the method of moments of the V-shaped antenna pattern and antenna current distribution)

the difference between images

说明：  多种窗函数的FIR滤波器，GUI界面，参数设置，滤波测试，图片显示(FIR filter of window functions, GUI interface, parameters setting，filter test, picture shows )

说明：   接收设备能对外界信息正确的接收、分析、处理的重要性是不言而喻的,它关系到社会的发展、人类的进步。然而在有用信号中往往伴随着噪声的存在。这种噪声分为电路内部固有的噪声与系统装置从外部接收而来的噪声。电路内部的噪声是系统固有的，与外界干扰无关，它可以通过对电路的合理设计、选择合理的元器件而得以控制。外部噪声主要由近几年的电子技术的高速发展，电子、电气设备的广泛应用而带来的电磁干扰引起的。这种噪声现象在工业发达的城市非常突出，它对各种系统接收装置的影响也越来越严重，使得这些系统装置不能正常工作，使得这些系统装置性能降低，甚至使得这些系统装置受到损坏。战胜噪声已是现代电子技术发展道路上必须逾越的巨大障碍。要解决噪声带来的问题需要我们从最基本的干扰源、信息传输通道（信道）、系统装置电路下手。(The receiving device and system noise)

Companding is essentially a technique for achieving non-uniform quantization. There are basically 2 methods which are most popular in literature.:-A-law companding and u-law companding. These algorithms help in achieving to an extend, compression. The given code shows how to compress a 14-bit code to 8-bit code and its digital implementation using matlab. Please contact by mail if a detailed description of code is needed.

本程序包包含两个程序和一个文档。两个程序为二进制与ASCII码之间的转换，文档为comtrade数据格式标准文档。(This package contains two procedures and documentation. Two programs for the conversion between binary and ASCII code, documentation comtrade data format standard document.)

能够测试不同码率（1/2,1/3）的卷积码在awgn信道下的性能(To test different bit-rate (1/2, 1/3) convolutional codes in awgn performance under channel)

将K均值算法用于图像分割,输入的是彩色图像,转换为灰度图像进行分割,输出结果为灰度图像.利用灰度做为特征对每个像素进行聚类,由于光照等原因,有时应该属于一个物体的像素,其灰度值也会有很大的差别,可能导致对该像素的聚类发生错误.在分割结果中,该物体表面会出现一些不同于其它像素的噪声点,因此,算法的最后,对结果进行一次中值滤波,以消除噪声,达到平滑图像的作用(The K means algorithm for image segmentation, the input is a color image, convert to grayscale image segmentation, the output of grayscale images. The use of gray as the characteristics of each pixel clustering, due to light and other reasons, and sometimes should belong to an object pixel, its gray value will also be very different, may lead to clustering of the pixel error has occurred. in the segmentation results, the surface, there would be different from other pixel noise points, so , the algorithm Finally, the results of a median filter to eliminate noise, to the role of smoothing the image)