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Kalman filter versus IMM estimator: when do we need the latter?

机译：卡尔曼滤波器与IMM估算器：何时需要后者？

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摘要

In this paper, a performance comparison between a Kalman filter and the interacting multiple model (IMM) estimator is carried out for single-target tracking. In a number of target tracking problems of various sizes, ranging from single-target tracking to tracking of about a thousand aircraft for air traffic control, it has been shown that the IMM estimator performs significantly better than a Kalman filter. In spite of these studies and many others, the condition under which an IMM estimator is desirable over a single model Kalman filter versus an IMM estimator are quantified here in terms of the target maneuvering index, which is a function of target motion uncertainty, measurement uncertainty, and sensor revisit interval. Using simulation studies, it is shown that above a certain maneuvering index an IMM estimator is preferred over a Kalman filter to track the target motion. These limits should serve as a guideline in choosing the more versatile, but somewhat costlier, IMM estimator over a simpler Kalman filter.
机译：在本文中，对单目标跟踪进行了卡尔曼滤波器和交互多模型（IMM）估计器之间的性能比较。在从单一目标跟踪到跟踪约一千架用于空中交通管制的飞机等各种大小的目标跟踪问题中，已证明IMM估计器的性能明显优于Kalman滤波器。尽管进行了这些研究和许多其他研究，但这里还是根据目标操纵指数来量化IMM估计器相对于单个模型Kalman滤波器而不是IMM估计器的条件，这是目标运动不确定性，测量不确定性的函数，以及传感器重访间隔。使用模拟研究表明，在一定的机动指数之上，IMM估计器比卡尔曼滤波器更可追踪目标运动。这些限制应作为选择更通用但更昂贵的IMM估计器而非更简单的Kalman滤波器的准则。

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