pda_matlab

说明：  精通matlab C++混合编程，很好的教程，希望对大家有用(Proficient in matlab c++ mixed programming)

Adams外插法程序，用于画16阶以内的稳定域。(Adams extrapolation procedure, used to draw the16 order within the stable region.)

智能天线技术 DOA估计-算法 波达方向估计(DOA estimation smart antenna technology- Operator Fa Boda direction estimation)

编写四阶 Runge_Kutta 公式的计算程序，对线性状态方程进行仿真 (Preparation of fourth order Runge_Kutta formula calculation procedures, the linear equation of state for simulation)

用于描述几何非线性的程序，大变形，位移加载或者应力加载(A program used to describe geometric nonlinearity, large deformation, displacement loading, or stress loading)

基于互功率谱的时延估计，esprit算法对有干扰的信号频率进行估计,包括 MUSIC算法，ESPRIT算法 ROOT-MUSIC算法。( Based on the time delay estimation of power spectrum, esprit algorithm signal frequency interference can be assessed Including the MUSIC algorithm, ESPRIT algorithm ROOT-MUSIC algorithm.)

Clonal selection algorithm with matlab, and applications

雷达系统仿真，采用相位编码脉内线性调频混合调制信号。该雷达具有数字化正交解调、脉冲压缩处理、固定目标对消、动目标检测（MTD）、和恒虚警（CFAR）处理等功能(Radar simulation system，using the LFM and barker code mixed modulated signal。Main function include：digital orthogonal demodulationfixed, Pulse compression，target cancellation, moving object detection (MTD), and the constant false-alarm (CFAR) processing etc)

时域序列x 和 h 的N点循环卷积子程序，用于matlab仿真调用(Time domain sequence x and h N-point circular convolution subroutines for the call of matlab simulation)

七单元天线阵MUSIC DOA估计： d=1 ， 天线阵元的间距； lma=2， 信号中心波长； 四输入信号； A=[A1,A2,A3,A4]， 得出A矩； 四信号的频率d=[1.3*cos(v1*n) 1*sin(v2*n) 1*sin(v3*n) 1*sin(v4*n)] 构造输入信号矢量 U=A*d 总的输入信号 总输入信号的协方差矩阵 [s,h]=eig(c) 求协方差的特征矢量及特征值 取出与零特征值对应的特征矢量 求协方差矩阵的逆矩阵 应用Music法估计输出 绘出各波达方向图(Seven-element antenna array MUSIC DOA estimates: d = 1, Antenna Array pitch LMA = 2 signal center wavelength four input signals A = [A1, A2, A3, and A4], drawn A moment tetra-frequency of the signal D = [1.3* cos (V1* n) 1* ⁢ sin (v2* n) 1* sin (v3* n) 1* sin (V4* n)] constructed input signal vector U = A* D of the total input signal of the total input signal covariance matrix [S] = EIG (c) seeking covariance feature vector and the feature value removing and corresponding to the zero eigenvalues ​ ​ characterized vector seeking covariance matrix inverse matrix Applications Music estimate output plotted DOA Figure)