粒子滤波器是通过蒙特卡罗模拟来实现递归贝叶斯滤波，它不需要线性、高斯噪声的假设，适用于任何能用状态空间模型表示的非线性系统，比卡尔曼滤波器的适用范围广。这里给出了几个粒子滤波的matlab编程实例。-Particle filters are using Monte Carlo simulations to achieve the recursive Bayesian filtering, it does not require linear, Gaussian noise assumptions

卡尔曼滤波及详细解释，本代码关于如何建立卡尔曼滤波原理-When I first studied Kalman filtering, I saw many advanced signal processing submissions here at the MATLAB Central File exchange, but I didn t see a heavily commented, basic Kalman filter present to allow someone new to K

文章介绍了卡尔曼滤波的原理，包括状态方程、过程估计、噪声原理等，并通过matlab予以实现验证。-This paper introduces the principle of Kalman filtering, including the equation of state, the process of estimation, noise theory, etc., and through matlab to achieve validation.