跳频系统仿真实例

说明：  关于软频率复用的matlab仿真，最后仿真出功率的分配(On the soft frequency reuse of matlab simulation, the final simulation the distribution of power)

一种求随机数的方法的实现，同余随机数，比混沌随机方法更好，能通过评测。(A Method for the Realization of the method of random numbers, random number with more than )

彩色图像融合处理 swt matlab程序并有整套评价程序和设计图片(Color image fusion swt matlab program and a full set of uation procedures and the design of the picture)

基于原始粒子群算法和原始模拟退火算法的结合。具有收敛性好，计算速度快的优势(Combined with the original particle swarm algorithm based on simulated annealing algorithm and the original. Has a good convergence, high calculation speed advantage)

波数域算法又称为距离徙动算法（Range Migration Algorithm，RMA）或-算法，由Cafforio等提出。该算法分析距离处理后信号的二维频谱，通过二维频域匹配滤波进行相位补偿，在完成方位聚焦的同时完全校正距离徙动。由于其中的Stolt变换能克服SAR信号中距离徙动和SRC对斜距的依赖，因此波数域算法是一种理想的成像算法，尤其在处理大斜视角和长合成孔径的SAR数据时。但Stolt变换需要插值，降低了处理效率并引入额外的误差。(The wavenumber domain Algorithm is also called Range Migration Algorithm (RMA) or -algorithm, which is proposed by Cafforio et al.The algorithm analyzes the 2-d spectrum of the signal after the distance processing, carries out phase compensation through the 2-d frequency domain matching filter, and completely corrects the distance migration while completing the azimuth focusing.Because the Stolt transform can overcome the SAR signal range cell migration and SRC dependence on oblique distance, thus wavenumber domain algorithm is an ideal imaging algorithm, especially when dealing with large squint Angle and long synthetic aperture of SAR data.But Stolt transformation requires interpolation, reduces processing efficiency and introduces additional errors.)

8阵元（均匀圆阵）的MUSIC算法matlab仿真程序(8 array elements (uniform circular array)-MUSIC algorithm matlab simulation program)

神经网络的 语音识别 初学者可以看看 希望对大家有帮助(Speech recognition neural network can look at beginners)

说明：  蚁群算法混合粒子群算法的matlab程序(Matlab Program of Ant Colony Algorithm Mixed Particle Colony Algorithm)

能够将扫描得到的指纹图像连接并去噪,以便能够更好地与指纹库中的图片进行匹配(the program can improve the connectivity of some fingerprint pictures.)

PURPOSE: [LLF, grad, hessian, h, scores, robustse] = garchlikelihood(parameters , data , p , q)