卡尔曼滤波器的算法C实现

最佳线性滤波理论起源于40年代美国科学家Wiener和前苏联科学家Ｋолмогоров等人的研究工作，后人统称为维纳滤波理论。从理论上说，维纳滤波的最大缺点是必须用到无限过去的数据，不适用于实时处理。为了克服这一缺点，60年代Kalman把状态空间模型引入滤波理论，并导出了一套递推估计算法，后人称之为卡尔曼滤波理论。卡尔曼滤波是以最小均方误差为估计的最佳准则，来寻求一套递推估计的算法，其基本思想是：采用信号与噪声的状态空间模型，利用前一时刻地估计值和现时刻的观测值来更新对状态变量的估计，求出现时刻的估计值。它适合于实时处理和计算机运算。-Kalman filter algorithm implemented in C

Optimal linear filtering theory originated in the 1940s, American scientists Wiener and the former Soviet Union scientists Kолмогоров research, and their descendants are collectively referred to as Wiener filtering theory. In theory, the biggest drawback of the Wiener filter is needed for unlimited data, does not apply to real-time processing. To overcome this shortcoming, in the 1960s, Kalman state space model of the introduction of filtering theory, and a recursive estimation algorithm is derived, later known as the Kalman filter theory. Kalman filter based on minimum mean square error of the estimated best practices, to seek a recursive estimation algorithm, the basic idea is: the state space model of signal and noise, the first time to estimate and the present moment the observed values ​ ​ to update the estimated state variables, find the estimated value of the moment. It is suitable for real-time processing and computing.