混合卡尔曼滤波的非均匀校正算法

提出了一种基于混合卡尔曼滤波的非均匀校正算法，可以用于解决参数漂移为非线性的问题．该方法首先根据给定状态空间模型的特点，使用无迹卡尔曼滤波技术和传统卡尔曼滤波技术分别处理非线性的状态转移模型和线性的观测模型，然后把估计结果用于非均匀校正．使用仿真的一维和二维数据验证算法性能，实验结果表明，所提算法扩展了传统卡尔曼滤波算法的使用范围，并且与扩展卡尔曼滤波算法相比，具有较高的稳定性和估计精度．

Nonumiformity correction algorithm of using mixed Kalman filter

A mixed Kalman filtering based nonuniformity correction algorithm is proposed, which can solve the problem of parameter drifts being nonlinear. Firstly, based on the feature of the given state-space model, the proposed algorithm employs the unscented and the traditional Kalman filtering techniques to deal with a nonlinear state transition model and a linear state observation model, respectively. Then, the estimated results can be used for nonuniformity correction. The performance of the proposed algorithm is demonstrated with simulated 1D and 2D data. Experimental results indicate that our new method extends the application range of the traditional Kalman filtering and possesses higher stability and estimation precision compared with the extended Kalman filtering algorithm.