本文提出了一种基于强跟踪滤波器的自适应故障预报方法，能够对一类带时变参数的非线性系统进行故障预报。
This paper presents an adaptive fault prediction method based on strong tracking filter, which can predict faults in a class of nonlinear time varying systems.
针对线性随机系统提出了一种改进强跟踪卡尔曼滤波器(MSTKF)。
A modified strong tracking Kalman filter (MSTKF) for linear stochastic systems is proposed.
针对卡尔曼滤波器对系统模型依赖性强、鲁棒性差和跟踪机动目标能力有限的问题，提出了一种新的利用混合模糊逻辑和标准卡尔曼滤波器的联合算法。
The Kalman filter has been commonly used in target tracking, however its performance may be degraded in presence of maneuver, low robustness and strong model dependence.
本文提出了一种基于强跟踪滤波器的自适应故障预报方法，能够对一类带时变参数的非线性系统进行故障预报。
Then two better methods that one of correction of model error and the other of nonlinear filter by Strong Tracking Filter were proposed.
本文提出了一种基于强跟踪滤波器的自适应故障预报方法，能够对一类带时变参数的非线性系统进行故障预报。
Then two better methods that one of correction of model error and the other of nonlinear filter by Strong Tracking Filter were proposed.
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