Employing the fundamental principle of linear filter model, an improved simulation method is developed based on a high-order filter model for randomly undulatory sea surface.
根据线性滤波器模型的基本原理，提出了一种改进的基于高阶滤波器的随机起伏海面仿真方法。
The results show that in the simulation of non-linear system model, this framework for Bayesian predictive filter can implement the tracking of simple motion and the orientation prediction.
实验结果表明，在非线性系统模型的仿真中，贝叶斯预测滤波框架能够较好的实现对简单物体运动的跟踪和方位的预测。
The simulation results show that MHE can solve the constrained linear system and has more effective estimation performance compared with Kalman filter strategy.
仿真结果表明：与卡尔曼滤波方法相比，MHE方法能处理系统约束，具有比卡尔曼滤波更好的估计性能。
The simulation results show that MHE can solve the constrained linear system and has more effective estimation performance compared with Kalman filter strategy.
仿真结果表明：与卡尔曼滤波方法相比，MHE方法能处理系统约束，具有比卡尔曼滤波更好的估计性能。
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