论文部分内容阅读
本文开始用一个最小平方估值器的推导来产生一个加速度输入矢量估值。文中首先研究了一种感受目标机动的检测器,其次研究了估值器、检测器和“简化”卡尔曼滤波器的组合,以形成一个跟踪机动目标的跟踪装置,最后介绍了一些模拟结果。文中首先阐述了假定目标机动的实际剩余和假定不机动的“简化”卡尔曼滤波器的理论剩余之间的关系。然后估值器计算了上述关系拟合的最好的等加速度输入矢量。其结果是一个输入矢量最小平方估值器,该估值器可以用来修正“简化”卡尔曼滤波器。因为典型的目标在相当长的时间花费在以固定方位和恒定速度方式飞行,所以要用一部检测器来防护,以防自动修正“简化”卡尔曼滤波器。仅当估算的输入矢量范数超过了门限时,才表示机动并实现修正。该跟踪系统容易实现,其跟踪能力在三个跟踪实例中进行了说明。
This article starts with a Least Square Estimator derivation to produce an estimate of the acceleration input vector. In this paper, we first investigate a detector that can detect the target maneuver, then study the combination of estimator, detector and “simplified” Kalman filter to form a tracking device to track the maneuvering target. Finally, some simulation results are introduced. The paper first describes the relationship between the actual residual of the assumed maneuver and the theoretical residual of a “simplified” Kalman filter that is assumed to be non-maneuverable. The estimator then calculates the best iso-acceleration input vector fitted by the above relationship. The result is an input vector least square estimator that can be used to correct for “simplified” Kalman filters. Because typical targets spend a considerable amount of time flying at fixed and constant speeds, a detector should be used to guard against self-correcting “simplified” Kalman filters. Only when the estimated input vector norm exceeds the threshold, it indicates maneuver and correction. The tracking system is easy to implement and its tracking capability is illustrated in three tracking examples.