Markov_chain

说明：  攻击垂直机动目标比例导引三维弹道仿真。matlab编写(Attacks on the proportion of vertical maneuvering target-guided three-dimensional trajectory simulation. matlab prepared)

老师给的关于最优估计的matlab程序，包括卡尔曼 无迹卡尔曼等等的demos 和相关的pdf学习文档(Optimal State Estimation)

剔除了bug完全可编译运行，是分析guider 滤波，设计其他平台如FPGA等算法的很好的参考。(Excluding the bug can be compiled to run fully, is to analyze the guider filter, designed for other platforms such as FPGA algorithms such as a good reference.)

利用matlab GUI实现的串口编程例子，实现了图像的灰度化并进一步用于视频监视控，最大信噪比的独立分量分析算法。( Use serial programming examples matlab GUI implementation, Achieve a grayscale image and further control for video surveillance, SNR largest independent component analysis algorithm.)

虚拟出版社系统 模拟出版社的业务流程 程序Demo(Virtual Press System Simulation of business process procedures Press Demo)

matlab牛顿法的函数和执行调用的结果(Newton method functionand the results )

CMOS集成电路基础知识，CMOS集成电路的接口电路，CMOS集成电路使用注意事项 (Basic knowledge of CMOS CMOS integrated circuit, integrated circuit interface circuit, CMOS integrated circuit use matters needing attention )

说明：  内附matlab常用命令，详细列举了各个命令的中英文对照，方便学习。(Matlab commonly used commands included in detail the various commands listed in Chinese and English to facilitate learning.)

说明：  EKF仅仅利用了非线性函数Taylor展开式的一阶偏导部分（忽略高阶项），常常导致在状态的后验分布的估计上产生较大的误差，影响滤波算法的性能，从而影响整个跟踪系统的性能。最近，在自适应滤波领域又出现了新的算法——无味变换Kalman滤波器（Unscented Kalman Filter-UKF）。UKF的思想不同于EKF滤波，它通过设计少量的σ点，由σ点经由非线性函数的传播，计算出随机向量一、二阶统计特性的传播。因此它比EKF滤波能更好地迫近状态方程的非线性特性，从而比EKF滤波具有更高的估计精度。 (EKF only uses non-linear function of the first-order Taylor expansion of some partial derivatives (ignoring higher order terms), often leading to the posterior distribution of the state estimates to generate large errors affect the performance of filtering algorithms, which affect the whole tracking system performance. Recently, the field of adaptive filtering algorithms and the emergence of new- and tasteless transform Kalman filter (Unscented Kalman Filter-UKF). EKF UKF filter is different from the idea that it points through the design of a small amount of σ by σ point spread through the nonlinear function to calculate the random vector first and second order statistical properties of the transmission. Therefore it is better than the EKF filter nonlinear characteristics equation of state approach, which is more than the EKF filter estimation accuracy.)

matlab解决抛物线法一维无约束最优化问题，(use matlab for paowuxian)