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Maneuver Target Tracking Based on Grey System Theory

机译:基于灰色系统理论的机动目标跟踪

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The result of maneuvering target tracking based on theory of kalman filter depends on the state model of target and the measurement model. When the state model of target is imprecise or is not described by liner space model of state, the result will be volatilization. However, the establishment of mathematical models is complex and difficult. Sometimes, it is impossible to establish accurate mathematical models. If the established model is adaptive to more situation, the filter will not be volatilization, even if the target is maneuvering. This paper provides a new algorithm by introducing grey model into kalman filter. In course of kalman filter, the forecasting value depends on the state model of target no longer. It forecasts next value by using a few forward estimated values with grey differential equation. It gets the current estimated value by using the forecasting value of previous moment and the observed value of current moment. Not only its accuracy is higher, but also its performance is better. Especially, during target maneuvering, the result of filter is better than the traditional method. Experiment results indicate that the way of maneuver target tracking is feasible.
机译:基于卡尔曼滤波理论的机动目标跟踪结果取决于目标状态模型和测量模型。当目标的状态模型不精确或状态的线性空间模型未描述时,结果将挥发。然而,数学模型的建立是复杂且困难的。有时,不可能建立准确的数学模型。如果已建立的模型适用于更多情况,则即使目标正在操纵,过滤器也不会挥发。通过将灰色模型引入卡尔曼滤波器,提供了一种新的算法。在卡尔曼滤波过程中,预测值不再取决于目标的状态模型。它通过使用一些带有灰色微分方程的正向估计值来预测下一个值。它通过使用先前时刻的预测值和当前时刻的观测值来获取当前估计值。它不仅精度更高,而且性能更好。特别是在目标操纵过程中,滤波效果优于传统方法。实验结果表明,机动目标跟踪方法是可行的。

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