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On the globalized likelihood function for exact track-to-track fusion at arbitrary instants of time

机译:关于全局似然函数,用于在任意时间进行精确的轨道间融合

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Tracking applications in distributed sensor scenarios often suffer from small bandwidths and time delayed transmissions. In order to save communication capacity, it is common to transmit locally preprocessed estimates on the target to a central fusion center. A global track is obtained by Track-to-Track fusion. If sensor links are only established temporarily, transmissions can only be performed irregularly. This leads to the challenge of decentralized decorrelation of local sensor tracks for a T2TF at arbitrary instants of time. In this paper, we present a modified likelihood function which employs the global covariance of the whole system. As a result, the tracks remain decorrelated and may be fused as independent information entities. Therefore, the central track fusion is independent of past estimates and obtains a globally optimal estimate.
机译:分布式传感器场景中的跟踪应用通常遭受带宽小和传输延迟的困扰。为了节省通信容量,通常将目标上的本地预处理估计值传输到中央融合中心。通过“轨道到轨道”融合获得全局轨道。如果仅临时建立传感器链接,则只能进行不定期的传输。这就带来了在任意时间点对T2TF进行局部传感器轨迹分散去相关的挑战。在本文中,我们提出了一种修正的似然函数,该函数利用了整个系统的全局协方差。结果,轨道保持去相关,并且可以被融合为独立的信息实体。因此,中央轨迹融合独立于过去的估计,并获得了全局最优估计。

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