EasyKrig3.0

粒子滤波算法，非常管用的运动估计和运动预测，mat源代码

LMS algorithm for adaptive filtering. Worth seeing.

说明：  《MATLAB数字信号处理85个实用案例精讲:入门到进阶》特点是主要通过案例说明在数字信号处理基础部分编程中常出现的错误和解决方法。《MATLAB数字信号处理85个实用案例精讲:入门到进阶》适用于数字信号处理的初学者，亦可供高年级本科生、研究生和科研工程技术人员参考。("Matlab Digital Signal Processing 85 Practical Cases: Introductory to Advanced" is characterized by cases that illustrate the errors and solutions that often occur in the basic programming of digital signal processing. "Matlab Digital Signal Processing 85 Practical Cases: Introduction to Advanced" is suitable for beginners in digital signal processing, and can also be used as a reference for senior undergraduates, graduate students, and scientific research and engineering technicians.)

说明：  matlab函数，很多有用的matlab函数。希望对大家的学习有用。(matlab function, a lot of useful matlab functions. Hope to learn useful.)

说明：  背景差分法对运动汽车的检测，对检测得到的汽车图像进行预处理和边缘提取(Background difference method for the detection of movement of motor vehicles, the vehicle has been detected image pre-processing and edge extraction)

This contains program for alising ...

Solving one dimensional Diffusion equation

二进制dpsk matlab实现仿真程序(dpsk modulation)

This Matlab Code models a Rayleigh fading channel using a modified Jakes channel model. A modified Jakes model chooses slightly different spacings for the scatterers and scales their waveforms using Walsh–Hadamard sequences to ensure zero cross-correlation. alpha_n = frac{pi(n-0.5)}{2M} and eta_n = frac{pi n}{M}, results in the following model, usually termed the Dent model or the modified Jakes model: R(t,k) = sqrt{frac{2}{M}} sum_{n=1}^{M} A_k(n)left( cos{eta_n} + jsin{eta_n} ight)cos{left(2pi f_d t cos{alpha_n} + heta_{n} ight)}. The weighting functions A_k(n) are the kth Walsh–Hadamard sequence in n. Since these have zero cross-correlation by design, this model results in uncorrelated waveforms. The phases ,! heta_{n} are initialized randomly and have no effect on the correlation properties. Matlab fast Walsh-Hadamard transform function is used to efficiently generate samples using this model.

Sparse Bayesian Learning