source-codes

sedumi是一个做二阶锥规划的很有用的MATLAB工具包，在进行阵列信号处理，波束优化设计方面有着很好的作用(sedumi is a second-order cone programming to do useful MATLAB tool kit, making array signal processing, beam optimal design has a very good role)

用于斜视聚束式SAR成像的非线性频率变标算法，有完整数学模型。(Nonlinear Frequency Scaling Algorithm for High Squint Spotlight SAR Data Processing)

Switching-mode DC-DC buck converters

本程序包主要有5个函数，分别实现了大地坐标与WGS84坐标的转换，卫星位置计算，电离层延迟模型，对流层延迟模型(Delay_of_ionosphere(）Delay_of_troposphere( ） GetSVPos() BLH_to_WGS84( B,L,H ) WGS84_to_BLH(X,Y,Z))

NLMS是LMS的一种改进算法。对该自适应滤波的朋友有一定启发(NLMS is an improved algorithm of LMS. The adaptive filtering friends have a certain inspiration)

本程序为偏最小二乘法和主成分分析的matlab程序用于建立光谱信息和所测样品浓度的关系，建立预测模型(This program is partial least squares and principal component analysis matlab program for the establishment of spectral information and the relationship between the concentration of the samples, the establishment of forecasting model)

WENO SCHEME HYBRIDIZED WITH COMPACT SCHEME FOR EULER EQUATIONS

Project on Image Restoration

说明：  克里格法（Kriging，KRG）是由法国统计学家 Matheron 于 1963 年提出的，为纪念南非金矿工程师 Krige（Krige, 1951）曾于 1951 年使用回归方法对空间场进行预测的开创性研究，Matheron 将该方法命名为 Kriging，最开始该方法常被用于对矿产储量分布情况进行预（Matheron, 1963）。目前该方法作为最常用的空间插值算法，被广泛应用于地理科学、环境科学、大气科学等研究中。(Kriging (KRG) method was proposed by Matheron, a French statistician, in 1963. In order to commemorate the pioneering research of Krige (1951), a gold mine Engineer in South Africa, who used regression method to predict the spatial field in 1951, Matheron named this method Kriging. At first, this method was often used to predict the distribution of mineral reserves (Matheron, 1963) . At present, as the most commonly used spatial interpolation algorithm, this method is widely used in geographical science, environmental science, atmospheric science and other research.)

说明：  核主成分分析方法是将主成分分析方法与核方法的有机融合，核心思想是通过引入核方法将输入空间的数据映射到特征空间，并在特征空间进行主成分分析，实现降维与提取非线性数据特征的双重目标。(The kernel principal component analysis (KPCA) is an organic fusion of principal component analysis (PCA) and kernel method. The core idea is to map the data from input space to feature space by introducing the kernel method, and carry out principal component analysis in feature space, so as to achieve the dual goals of dimensionality reduction and feature extraction of nonlinear data.)