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Fusion-Based Multidetection Multitarget Tracking With Random Finite Sets
IEEE Transactions on Aerospace and Electronic Systems ( IF 5.1 ) Pub Date : 2021-02-12 , DOI: 10.1109/taes.2021.3059093
Lin Gao , Giorgio Battistelli , Luigi Chisci , Alfonso Farina

Multidetection (MD) systems are characterized by multiple observation modes (OMs), and hence, simultaneously produce multiple measurements for each target. The key challenge in exploiting MD systems for multitarget tracking (MTT), compared to single-detection (SD) systems, is the significant amount of extra computational burden involved in order to solve the resulting multidimensional assignment problem among measurements, targets, and OMs. This article presents a novel computationally efficient MTT framework for MD systems, wherein the multitarget state is modeled as a random finite set (RFS), and a bank of OM-dependent MTT RFS filters with SD model are employed to recursively provide OM-dependent posteriors. The latter, which contain both real and false targets, are then suitably fused so as to enhance consensus on the true targets while weakening trust on the existence of the false ones. In this way, the computational complexity is significantly reduced compared to existing MTT algorithms with the MD model. Two representative RFS filters, i.e., unlabeled probability hypothesis density (PHD) and labeled multi-Bernoulli (LMB), are considered in the proposed framework and the computational complexity of the resulting MD MTT algorithms is analyzed. Performance of the proposed approach is assessed by simulation experiments in both over-the-horizon-radar (OTHR) and single-frequency-network passive radar (SFN-PR) MTT applications.

中文翻译:

随机有限集的基于融合的多检测多目标跟踪

多重检测 (MD) 系统的特点是多种观察模式 (OM),因此可以同时为每个目标生成多个测量值。与单检测 (SD) 系统相比,利用 MD 系统进行多目标跟踪 (MTT) 的主要挑战是为了解决由此产生的测量、目标和 OM 之间的多维分配问题,需要大量额外的计算负担。本文提出了一种用于 MD 系统的新型计算高效 MTT 框架,其中将多目标状态建模为随机有限集 (RFS),并采用一组具有 SD 模型的依赖于 OM 的 MTT RFS 滤波器来递归地提供依赖于 OM 的后验. 后者包含真实和虚假的目标,然后适当融合,以增强对真实目标的共识,同时削弱对虚假目标存在的信任。通过这种方式,与现有的具有 MD 模型的 MTT 算法相比,计算复杂度显着降低。在所提出的框架中考虑了两个具有代表性的 RFS 滤波器,即未标记的概率假设密度 (PHD) 和标记的多伯努利 (LMB),并分析了由此产生的 MD MTT 算法的计算复杂性。通过在超视距雷达 (OTHR) 和单频网络无源雷达 (SFN-PR) MTT 应用中的模拟实验来评估所提出方法的性能。在所提出的框架中考虑了两个具有代表性的 RFS 滤波器,即未标记的概率假设密度 (PHD) 和标记的多伯努利 (LMB),并分析了由此产生的 MD MTT 算法的计算复杂性。通过在超视距雷达 (OTHR) 和单频网络无源雷达 (SFN-PR) MTT 应用中的模拟实验来评估所提出方法的性能。在所提出的框架中考虑了两个具有代表性的 RFS 滤波器,即未标记的概率假设密度 (PHD) 和标记的多伯努利 (LMB),并分析了由此产生的 MD MTT 算法的计算复杂性。通过在超视距雷达 (OTHR) 和单频网络无源雷达 (SFN-PR) MTT 应用中的模拟实验来评估所提出方法的性能。
更新日期:2021-02-12
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