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A multisensor-multitarget data association algorithm for heterogeneous sensors

机译：异构传感器的多传感器多目标数据关联算法

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摘要

The problem of associating data from three spatially distributed heterogeneous sensors with three simultaneous detections for all three is discussed. The sensors can be active or passive. The source of a detection can be either a real target, in which case the measurement is the true observation variable of the target plus measurement noise, or a spurious one, i.e. a false alarm. The sensors may have nonunity detection probabilities. The problem is to associate the measurements from sensors to identify the real targets, and to obtain their position estimates. Mathematically this leads to a generalized 3D assignment problem, which is known to be NP-hard. An algorithm suited for estimating the positions of a large number of targets in a dense cluster, using a fast, but nearly optimal, 3D assignment algorithm, is presented. Performance results for several representative test cases with 64 targets are presented.
机译：讨论了将来自三个空间分布的异构传感器的数据与所有三个同时进行的三个同时检测相关联的问题。传感器可以是主动或被动的。检测的源可以是真实目标，在这种情况下，测量值是目标的真实观测变量加上测量噪声，也可以是假信号，即错误警报。传感器可能具有不统一检测概率。问题在于将传感器的测量结果关联起来以识别实际目标，并获得其位置估计值。从数学上讲，这会导致一个广义的3D分配问题，这被认为是NP难题。提出了一种适用于估计密集目标中大量目标位置的算法，该算法使用了快速但几乎最佳的3D分配算法。给出了具有64个目标的几个代表性测试用例的性能结果。

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