LinearShootingMethod

这个函数用于估算光谱中的最大信息熵，它同时满足了倒频谱的局限和相关谱的局限(This function provides an ARMA spectral estimate which is maximum entropy satisfying correlation constraint (number of poles)and cepstrum constrains (number of ceros))

实现奇异值分解，可以去除随机噪声，在物探领域应用广泛(Realize the singular value decomposition and reconstruction)

Plot tutorial is using to calculate in Histogram Matlab

一种流形学习算法（很好用），计算互信息非常有用的一组程序,车牌识别定位程序的部分功能。( A fluid manifold learning algorithm (good use), Mutual information is useful to calculate a set of procedures, Part of the license plate recognition locator feature.)

说明：  3GPP LTE上行虚拟MIMO中的一种调度算法源程序代码(3GPP LTE uplink virtual MIMO scheduling algorithm in a source code)

说明：  基于simulink的SVPWM仿真，自己编写的，最后还差一个三相桥，大家可自行加上(Based on SVPWM simulation simulink, I have written, and finally a three-phase bridge worse, we can add their own)

Joomla Component Development

说明：  这个是语音信号处理里面的基于MMSE的谱减法增强处理程序，此种方法是我见过消除噪声最好的！(This is the speech signal processing inside the MMSE-based spectral subtraction to enhance handling procedures, this method is the elimination of noise, the best I' ve ever seen!)

该程序仿真了机载雷达对地面微动目标成像过程及结果。验证了微动的成对回波理论及微动对雷达成像造成的影响，改变微动参数，可看到鬼影点、灰度条带、直线圆列等由于微动参数不同而产生的不同现象。(refer to the chinese introduction)

统的联合概率数据关联算法(IPDA)是在密集杂波环境下的一种良好的多目标跟踪算法， 但它是针对单传感嚣对多目标跟踪的情况下使用，不能直接用于多传感器对多目标的跟踪。针对这 一问题，文中提出了一种适用于多传感器多目标跟踪的JPDA算法，它以极大似然估计完成对来自 多传感器的测量集合进行同源最优分划，然后采用JPDA方法对多目标进行跟踪。经过理论分析和 仿真试验，证明了该方法能有效地进行多传感器多目标的跟踪，且具有算法简单、跟踪精度高、附 加的计算量小等优点。(Abstract)m JointProbabilisticDataAssociation(JPDA)solves single sensormultitatgcttrackingin ClUtter,butitc锄not bc used directly in mnitisensor multitarget tracking．喇8 paper presents a method to implement multi—sensor multi—target tracking by combining maximum likelihood estimation wim JPDA The maximum Iikelihood estimation is used to classify the same source observations at one time into the same set,and血en JPDA implements multi—target uacking after the position fitsed．The theory analysis and computer simulation show that this algorithm may achieve multi—sensor multi—target tracking perfectly with low calculation load added and higher precision 。)