AKF

The adaptive Kalman filtering ( AKF) based on intelligent information fusion algorithm has currently became an effective approach to enhance the integrated navigation system's robustness and accuracy.

针对当前自适应组合导航系统算法的研究趋势，总结了卡尔曼滤波技术的缺陷和利用智能融合技术提高滤波器性能的设计思想。

In the end, we apply EM Algorithm with those improvements ( A-ECM and AKF) in identifying the high dimension gene regulatory network and solving the time-varying noise correlation matrices case.

最后，在基因序列分析中应用以上改进的EM算法来处理高维数和噪声时变情况下基因规则网络的估计问题，取得了良好的效果。

The results indicate that the precision of AKF output monitor signal is distinctly more excellent than general Kalman filter.

结果表明自AKF输出监控信号精度明显优于常规卡尔曼滤波；

Considering the system with time-varying noise correlation matrices, instead of standard Kalman filtering we use the improved adaptive Kalman filtering ( AKF) to estimate the hidden system states.

针对参数估计问题中具有时变噪声协方差阵的系统，本文提出了采用改进的自适应Kalman滤波代替标准EM算法中的Kalman滤波，实现对隐含系统状态的估计。

Application of the Integrated Alignment Method in AUV Based on AKF

基于AKF的组合对准方法在AUV中的应用

The AKF model of nonlinear OMS has been establish. The initial AKF parameters has also been set up to optimize the performance of the filter according to the abruptly variational Signal-to-Noise ( SNR) of the OMS Signal.

建立了针对非线性OMS的AKF模型，仿真分析中针对存在突变信噪比（Signal-to-Noise，SNR）的输入信号设置相应AKF初始参数使滤波性能达到最优。

In view of the present research situation of AKF technique, this paper discusses an AKF algorithm for on line estimation of the statistical feature of measurement noise, which has many advantages such as simple structure, realtime and practicable in engineering.

针对AKF技术的研究现状，本文探讨一种算法结构比较简单、实时性较强、工程上比较实用的在线估计量测噪声统计特性的AKF算法。