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首页> 外文期刊>Advances in Science, Technology and Engineering Systems >Estimation of Target Maneuvers from Tracked Behavior Using Fuzzy Evidence Accrual
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Estimation of Target Maneuvers from Tracked Behavior Using Fuzzy Evidence Accrual

机译：使用模糊证据权重从跟踪行为估计目标机动

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While the Kalman filter, including its many variants, has been the staple of the tracking community, it also has been shown to have drawbacks, particularly when tracking through a maneuver. The most common issue is a lag in the position of the target track compared to the true target position as the target performs its maneuver. Another more problematic issue can occur where the filter covariance collapses, requiring the filter to be reinitialized. Techniques exist to compensate for maneuvers, but generating their response relies on detection of error between the estimated trajectory and the measured target position. In this effort, a maneuver detection routine is developed that can be used in conjunction with more standard maneuver compensation approaches. This routine is able to validate the existence of a maneuver more quickly than use of the inherent detection relied upon in the other methods. Maneuver detection is performed by an evidence accrual system that uses a fuzzy Kalman filter to incorporate new information and provide a level of evidence that maneuver is occurring. The input data uses behavior characteristics of the Kalman gain vector from the tracking algorithm.
机译：尽管卡尔曼滤波器（包括其许多变体）一直是跟踪社区的主要内容，但它也显示出缺点，特别是在通过机动进行跟踪时。最常见的问题是在目标执行机动时目标轨道的位置与真实目标位置相比存在滞后。另一个更成问题的问题可能发生在滤波器协方差崩溃的地方，需要重新初始化滤波器。存在补偿机动的技术，但是产生其响应依赖于对估计的轨迹和所测量的目标位置之间的误差的检测。在这种努力下，开发了可与更标准的机动补偿方法结合使用的机动检测程序。与使用其他方法中依赖的固有检测相比，此例程能够更快地验证操作的存在。机动检测由证据累积系统执行，该系统使用模糊卡尔曼滤波器合并新信息并提供一定水平的证据表明正在发生机动。输入数据使用来自跟踪算法的卡尔曼增益向量的行为特征。

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