为了提高在杂波环境下跟踪强机动目标的精度，提出了一种新的基于期望极大化(EM)算法的机动目标状态估计方法。

To improve the accuracy of tracking the complex maneuver target in cluttered environment, a new state estimation algorithm based on the expectation maximization (EM) algorithm is presented.

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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.

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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。

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.

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