bifcircular

matlab文件操作的典型 例子，欢迎指正(matlab file operations of a typical example, please correct me)

Moving sign application

Matlab中没有现成的Lagrange插值函数，必须编写一个M文件实现Lagrange插值。 设n个节点数据以数组x0,y0 输入（注意Matlat的数组下标从1开始），m 个插值点以数组 x输入，输出数组y 为 m个插值。 编写一个名为lagrange.m的M文件。(Matlab is not off-the-shelf Lagrange interpolation function, we must realize the preparation of an M file Lagrange interpolation. N nodes based data to an array of x0, y0 input (Note Matlat subscript of the array starting at 1), m a x interpolation points to the array input and output array y for m months interpolation. Lagrange.m the preparation of a M-file.)

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.)

说明：  matlab下的kalman实现，内附详细的解释，有助于初学者学习，望高手免进(matlab kalman under implementation, containing a detailed explanation to help beginners learn to avoid looking into the master)

详细介绍主元分析的步骤、用matlab具体的实现方式(Details the steps of the principal component analysis using matlab specific way to achieve)

对学cc++的有帮助，，多看看，研究，，研究(Have help in learning java,, more to see, study, study)

Matlab code for MRC with correlation

说明：  多目标跟踪，可处理多个目标，但8个以上效果就不行了。(Tracking, can handle multiple targets, but more than 8 results does not.)

高等數位訊號處理第九章作業 可以參考看看 裡面還有其他章作業(Advanced Discrete-Time Signal Processing )