A modified strong tracking Kalman filter (MSTKF) for linear stochastic systems is proposed.
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 new multisensor distributed track fusion algorithm is put forward based on combining the feedback integration with the strong tracking Kalman filter.
研究基于强跟踪滤波和反馈综合相结合的多传感器分布式航迹融合算法。
The results of simulation indicate that this new approach has a better accuracy than the traditional Kalman filter and strong tracking Kalman filter in the field of maneuvering target tracking.
仿真结果表明，该改进滤波器跟踪机动目标的精度高于常规卡尔曼滤波器和强跟踪卡尔曼滤波器。
For satellite formation flying orbit adjustment situation, which only used radio measurement, a strong tracking unscented Kalman filter (UKF) algorithm was introduced to the simulation.
并针对编队卫星进行轨道机动时仅采用无线电进行测量的工况，采用强跟踪离散卡尔曼滤波(ukf)算法进行仿真计算。
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.
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.
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。
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