radar-system-analysis-using-matlab

数值计算，能够计算方程组，矩阵，能够自己输入数据进行计算(Numerical calculation, to calculate equations, matrices, to make their own input data calculation)

针对二维矩形条带装箱问题提出了一种启发式布局算法，即底部左齐择优匹配算法（lowest—level left align bestfit，简称LLABF）．LLABF算法遵循最佳匹配优先原则，该原则综合考虑完全匹配优先、宽度匹配优先、高度匹配优先、组合宽度匹配优先及可装入优先等启发式规则．与BL（bottom-left），IBL（improved-bottom-left）与BLF（bottom—left-fill）等启发算法不同的是，LLABF能够在矩形装入过程中自动选择与可装区域匹配的下一个待装矩形．计算结果表明，LLABF结合遗传算法（genetie algorithm,简称GA）解决二维条带装箱问题更加有效．(A heuristic placement algorithm for two-dimensional rectangular strip packing problem is proposed, which is called bottom-level left align best fit algorithm (LLABF). Different from BL (bottom-left), IBL (improved-bottom-left) and BLF (bottom-left-fill) heuristic algorithms, LLABF can automatically select the next rectangle to be loaded that matches the mountable region during the rectangular loading process. Genetie algorithm (GA) is more effective to solve the two-dimensional strip packing problem.)

用于仿真雷达基础函数-线性调频函数和实验结果分析(simulate LFM for radar, results are also include.)

创建了为主成分分析所需的数据矩阵，并通过主成分分析完成对所得数据的处理。(Analysis of the main ingredients to create the required data matrix, and principal component analysis of the data to complete the deal.)

自己根据网上已有程序改写的(127,115)RS编码，有详细的注释及对FPGA实现算法的改写(参考try123.m)，希望可以让大家少走弯路~~~((127,115) rs encoder/decorder with detailed annotations.)

做同步可能用到的几种常见的序列，对做同步的同学有很到帮助。(Synchronization may be used in several common sequence)

arma模型，利用matlab来实现的，arma模型的matlab代码实现。可以实现任意步数的预测。(arma model using matlab.You can achieve any number of steps predicted. )

这个程序是用matlab 编写的GMSK的调制程序希望对大家有用(This program is to use matlab GMSK modulation of the program for people to use)

支持向量机是用于分类与回归的技术。由于其自身的诸多缺点，如无法获得概率输出，需要估计一个误 差参数C，以及必须使用Mercer 核函数等。相关向量机算法，克服了SVM 上述缺点，RVM 能获得与SVM 相比拟的推 广性能，并且更为稀疏。在此基础上，文中介绍了一种RVM 回归用于分类的新分类方法，用RVRC 来表示。并通过 实验证明了它的可行性。(Support vector machines for classification and regression techniques. Due to its many shortcomings, such as the probability that the output can not be obtained, need to estimate an error parameter C, and the need to use Mercer kernel function and so on. Relevance vector machine algorithm to overcome these shortcomings of SVM, RVM and SVM can be obtained comparable to promote performance and more sparse. On this basis, the paper introduces a RVM regression was used to classify new classification method, RVRC to represent. And through experiments proved its feasibility.)

Matlab最优化计算中的无约束多维极值问题(Matlab Optimization of unconstrained multi-dimensional calculation of extremal problem)