GA---Genetic-Algorithm--Matlab

用matlab实现图像融合，并且具有图片(Achieve image fusion using matlab)

目标跟踪问题的应用背景是雷达数据处理，即雷达在搜索到目标并记录目标的位置数据, 对测量到的目标位置数据（称为点迹）进行处理，自动形成航迹，并对目标在下一时刻的位置进行预测。 ben文简要讨论了用Kalman滤波方法对单个目标航迹进行预测，并借助于Matlab仿真工具，对实验的效果进行评估。 trajectory.m产生理论的航迹，并绘出 Kalman_filter.m 利用Kalman滤波算法，对目标的航迹进行估计 filter_result.m kalman滤波估计的结果，以及估计误差的均值和标准差曲线(target tracking application context radar data processing, radar in search of the goals and objectives of the record location data, for the measurement of target location data (known as trace points), automatic track, as well as the next target location for the moment forecast. Ben paper with a brief discussion of Kalman filtering method to a single target track forecast and using Matlab tool for experimental evaluation of effectiveness. Trajectory.m have theoretical track, and draws Kalman_filter.m using Kalman filtering algorithm, the target track estimated filter_result.m Kalman filtering results, and the estimated error of the mean and standard deviation of curve)

这是数学建模中会用到的各种算法的总结书，内容很好，希望能有所帮助。(This is the mathematical modeling will be used in a variety of algorithms summarize the book, the content well, hoping to help.)

MIMO系统下软判决译码。仿真软件是使用MATLAB进行仿真，(Soft decision decoding of MIMO systems. )

说明：  包括建初始种群，编码，适应值计算，以及交叉、变异、重组等源码(Including building the initial population, coding, adaptation values, as well as crossover and mutation or re-source)

example in computer networks

说明：  基于matlab神经网络工具箱bp神经网络在去噪声方面的应用。(Applications based on neural network toolbox matlab bp neural networks to noise aspects.)

打包文件中含有完整的meanshift算法的实现过程，可以在matlab中运行查看(Archive file contains the complete meanshift algorithm implementation process, you can run to see in matlab)

说明：  Hurst exponent calculation algoritms. Time series analysis trending, mean reverting. Analysis of time series, fractal analysis.

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. )