NONSAP

二维热传导扩散方程的高斯-赛德尔迭代方法。(A two-dimensional thermal diffusion problem using Gauss-Seidel iteration method.)

使用拉格朗日乘子法解约束优化问题，例子是最优化问题，有3个线性约束(Using the Lagrange multiplier method to solve the constrained optimization problem)

说明：  分别对beta估计值模拟分析、非中心对称分布、卡方拟合优度检验、筛选抽样随机数抽取、有限样本性质模拟这5个内容进行了编程说明(The five contents of beta estimation simulation analysis, non-centrosymmetric distribution, chi-square goodness-of-fit test, random number extraction of screening sampling and Simulation of finite sample properties are explained by programming.)

以随机的中弱大气湍流为背景，可用于计算大气湍流下准直高斯光束的光强分布，光束扩展以及光束角漂移(The random weak atmospheric turbulence as the background can be used to calculate atmospheric turbulence of collimated Gaussian beam intensity distribution and beam propagation and beam angle drift)

外点牛顿法，十个参数，可以解决带有约束条件的非线性规划问题。(External point Newton, with dozens of parameters that can solve linear programming problems with constraints)

说明：  运用数组，指针，通过最简单的c语言完成矩阵的转置计算。(Using array, pointer, through the simplest c language to complete the matrix transpose calculation)

很有用的一篇国外关于FDTD算法的学术论文，相信对初学者有很大帮助(Useful in a foreign academic papers on the FDTD method, I believe very helpful for beginners)

针对多径效应的影响，提出了一种基于矩阵束的MIMO 雷达低仰角快速估计方法。该方法同时考虑了发射多径信号和接收多径信号，采用单样本数信号矢量构造了一个前后向矩阵束，并利用两个酉矩阵对该矩阵束进行降维处理，最后采用广义特征值分解的总体最小二乘法来估计目标角度。算法不需要估计数据协方差矩阵，可在低 信噪比和单样本数情况下，有效地克服多径效应，实现同时多目标低仰角估计，相比最大似然算法，避免了谱峰搜索，计算量小。仿真结果验证了该算法的有效性。(To overcome the multipath effect, a fast algorithm for low elevation estimation for MIMO radar via matrix pencil is proposed. The signals both in transmit multipath and receive multipath are considered. Firstly, a forward-backward matrix pencil is formed based on the single-sampled vector. Secondly, the reduced-dimensional processing is applied to the matrix pencil via two unitary matrices. Finally, the generalized eigen-decomposition is employed to estimate the low elevation in multipath environments directly. Without the covariance matrix estimation, the proposed method can overcome multipath effect effectively in case of low SNR and estimate multi-target simultaneously. Compared with Maximum Likelihood (ML) method, it avoids spectrum peak searching and reduces the computational burden. Numerical results verify the effectiveness of this method)

说明：  分数傅里叶变换和分数余弦变换，动态展示实部，虚部和模值(Computation of the Fractional Fourier Transform)

空间目标识别，采用PM算法，线性调频脉冲压缩的Matlab程序,多抽样率信号处理。( Space target recognition algorithm using PM, LFM pulse compression of the Matlab program, Multirate signal processing.)