4e

Matlab中的主成分分析函数，对初学者比较适用(Matlab in principal component analysis function, more suitable for beginners)

A sample code on fractional order derivatives

matlab入门基础源代码，一共八个文件夹，上百个matlab源程序，对学习matlab可能会有帮助。(Getting the underlying source code matlab, might help)

simple plot and show image matlab code with dll for .net app

主要内容有：雷达数据处理概述（包括研究目的、意义、历史和现状等），参数估计与线性滤波方法，非线性滤波方法，量测数据预处理技术，多目标跟踪中的航迹起始，极大似然类多目标数据互联方法，贝叶斯类多目标数据互联方法，机动目标跟踪，群目标跟踪，多目标跟踪终结理论与航迹管理，无源雷达数据处理，脉冲多普勒和相控阵雷达数据处理，雷达组网数据处理，雷达数据处理性能评估，雷达数据处理仿真技术，雷达数据处理的实际应用，以及关于雷达数据处理理论的回顾、建议与展望。(The main contents are: an overview of the radar data processing (including research purpose, meaning, history and current status, etc.), parameter estimation method with linear filtering, nonlinear filtering method, measurement data preprocessing techniques, starting track multiple target tracking, maximum Likelihood class of multi-target data interconnection methods, Bayesian methods class multi-target data association, target tracking, target tracking group, multi-target tracking end theory and track management, passive radar data processing, pulsed Doppler and phased array radar data processing, radar network data processing, radar data processing performance evaluation, radar data processing simulation technology, the practical application of radar data processing, as well as a review on radar data processing theory, suggestions and prospects.)

一个关于LQG控制的简单例子，对相关函数的调用的理解相当有帮助，亲测有效实用。(A simple example of LQG control is very helpful to understand the call of related functions, and it is effective and practical to test personally.)

图像分割中的分水岭算法，且用matlab实现的。(The watershed segmentation algorithm, and implemented with matlab.)

输入任意文法，找到终结符和非终结符。并统计个数。()

超宽带技术的仿真，可以正常运行，一个师兄的(ｕｗｂ)

MIMO雷达模型下一种子空间谱估计方法，采用过估计的方法，以避免信源数估计的问题，直接对数据协方差矩阵进行变换，从而构造了信号子空间投影矩阵和噪声子空间投影矩阵，不需要像经典的MUSIC一样对其进行特征分解，完全避开了在一般非理想情况下MUSIC算法必须面对的识别小特征值与大特征值的麻烦，降低了复杂度，而且该方法不受快拍数的影响，在相干源情况下也能准确的估计目标的入射角，不会出现伪峰。(A subspace based DOA (Direction-Of-Arrival) estimation method for MIMO (Multiple-Input Multiple-Output) radar is studied in this paper. By transforming the data covariance matrix, we can construct the projection matrix of signal subspace and noise subspace. The computation complexity of the algorithm is reduced as it does not need to perform eigendecomposition. By using the over estimated theory, this algorithm avoid estimating the number of signal sources and can keep a good estimate performance in the case of lower SNR (Signal-to-Noise Ratios) and multi-target. )