具有无源传感器的分布式MHT

S. Coraluppi, C. Rago, C. Carthel, Brandon Bale
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引用次数: 2

摘要

本文主要研究了无源传感器多目标跟踪中的两个问题。首先是众所周知的可观测性问题,即单个传感器的测量不足以定位目标。其次,需要放宽通常的小目标假设,即每次扫描每个目标最多测量一次。事实上,在被动声纳等一些应用中,存在重复测量,即一个传感器每次扫描对每个目标进行多次检测。我们研究了多传感器环境下的这些挑战,并描述了分布式MHT解决方案架构的优势,该架构采用测量空间跟踪,然后使用鲁棒笛卡尔初始化方案进行多传感器笛卡尔跟踪。在重复测量的情况下,存在(至少)两种可行的处理体系结构。在这两种情况下,我们都利用了最近开发的对MHT递归的泛化。我们研究了这两种备选方案的相对优点。
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Distributed MHT with Passive Sensors
This paper focuses on two challenges in multi-target tracking with passive sensors. The first is the well-known observability problem whereby individual sensor measurements are insufficient to localize targets. The second is the need to relax the usual small-target assumption of at most one measurement per target per scan. Indeed, in some applications such as passive sonar, there are repeated measurements, i.e. multiple detections per target per scan of one sensor. We examine these challenges in a multi-sensor setting and describe the advantages of a distributed MHT solution architecture, with measurement-space tracking following by multi-sensor Cartesian tracking using a robust Cartesian initialization scheme. In the presence of repeated measurements, there are (at least) two viable processing architectures. In both cases we leverage a recently developed generalization to the MHT recursion. We study the relative merits of the two alternative solutions.
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