以指数趋势模型为基础，建立卡尔曼滤波模型对链子崖危岩体ga监测点的位移量进行预测。
Based on the exponential trend model, the Kalman filter model is applied to forecasting displacement values at observation point in Lianziya hazardous rock mass GA.
时域分析包括时域波形回放、数字滤波、轴心轨迹分析、解调分析、趋势分析、相关分析。
The time field analysis module contains the timely wave return, digital filter, axis center track analyzing, demodulation analyzing, trend analyzing, mutual analyzing.
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。
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.
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