arithmetic_main

卡拉曼+强跟踪+自适应滤波算法应用于汽车做二维运动(Karaman+ strong track+ adaptive filtering algorithm used in the car to do the two-dimensional movement)

再现全息图，读入图片再现全息程序，该路径后可运行，用再现光的方法(Reproducing hologram reproducing holographic read into the program, the path can be run with the method of reproducing light)

用Jacobi Gauss-seidel SOR迭代法求解线性方程组(With Jacobi Gauss-seidel SOR iterative method for solving linear equations)

Rebel工具包，包括UKF，CDKF在内的多种粒子滤波算法仿真，是非线性系统状态估计的良好帮手(Rebel kit, including the UKF, CDKF including a wide range of particle filter simulation, nonlinear system state estimation is a good helper)

求局部最小值与卷积，lmax为MATLAB的历程 工程里是具体的c函数(Find local minimum and convolution, lmax for the project in the history of MATLAB is the specific function of c)

function yhat = logistic(bayta,X)

采用熵值法求特征分量的权重，一般可用于特征选择与特征提取(Entroy algorithm ia used to be calculate the weight of each element,and is helpful in the selection and extraction of features)

将CKF与UKF融合在一起，很好的解决了CKF应对状态突变的问题(The CKF and UKF together, a good solution to the problem CKF respond mutation status)

先对数据进行ARMA建模，再在ARMA建模的基础上进行Kalman滤波已经运行通过，而且效果很好(ARMA modeling data first, and then on the basis of ARMA modeling performed by the Kalman filter has been running, and the effect is very good)

上传一个word档的联邦式扩展卡尔曼粒子滤波算法，大家学习粒子滤波有益，为了使联邦滤波器够有效处理非高斯、非线性系统的状态估计问题，提出将扩展卡尔曼粒子滤波引入联邦滤波结构中，得到一种新的联邦式扩展卡尔曼粒子滤波算法.使用扩展卡尔曼粒子滤波对联邦滤波子系统的多源数据进行处理，从而摆脱了经典卡尔曼滤波的限制，拓宽了联邦滤波器的实际应用范围.将联邦式扩展卡尔曼粒子滤波算法应用于非线性滤波器的一个标准验证模型进行了仿真实验，结果表明该算法是有效性的.(Abstract: A new particle filter(Federated Extend Kalman Particle Filter，EKF-FPF) is proposed to estimate the state of Non-Gaussian and Non-Linear system for federated filter, in which extend kalman particle filer is introduced to federated filter so that the information fusion of subsystem can be solved by the non-gaussian and non-linear filer. By doing so, the federated filter can get rid of the disadvantage of the ordinary kalman filter to extend its application field. The simulation results of the standard testing model demonstrate the feasibility of the proposed algorithm.)