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首页> 外文期刊>IEEE Transactions on Aerospace and Electronic Systems >Dynamic Cramer-Rao bound for target tracking in clutter
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Dynamic Cramer-Rao bound for target tracking in clutter

机译：动态Cramer-Rao约束可用于杂波中的目标跟踪

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摘要

Recently, there have been several new results for an old topic, the Cramer-Rao lower bound (CRLB). Specifically, it has been shown that for a wide class of parameter estimation problems (e.g. for objects with deterministic dynamics) the matrix CRLB, with both measurement origin uncertainty (i.e., in the presence of false alarms or random clutter) and measurement noise, is simply that without measurement origin uncertainty times a scalar information reduction factor (IRF). Conversely, there has arisen a neat expression for the CRLB for state estimation of a stochastic dynamic nonlinear system (i.e., objects with a stochastic motion); but this is only valid without measurement origin uncertainty. The present paper can be considered a marriage of the two topics: the clever Riccati-like form from the latter is preserved, but it includes the IRF from the former. The effects of plant and observation dynamics on the CRLB are explored. Further, the CRLB is compared via simulation to two common target tracking algorithms, the probabilistic data association filter (PDAF) and the multiframe (N-D) assignment algorithm.
机译：最近，对于一个老话题Cramer-Rao下界（CRLB），有一些新结果。具体而言，已经表明，对于各种各样的参数估计问题（例如，对于具有确定性动力学的对象），具有测量起点不确定性（即，在存在虚假警报或随机杂波的情况下）和测量噪声的矩阵CRLB是简单地说，没有测量原点不确定性乘以标量信息缩减因子（IRF）。相反，对于随机动态非线性系统（即，具有随机运动的物体）的状态估计，已经出现了一种简洁的CRLB表达式。但这仅在没有测量原点不确定性的情况下有效。本文可以视为两个主题的结合：保留了后者的巧妙的类似Riccati的形式，但包括了前者的IRF。探索了植物和观察动力学对CRLB的影响。此外，通过仿真将CRLB与两种常见的目标跟踪算法进行比较，即概率数据关联过滤器（PDAF）和多帧（N-D）分配算法。

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