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Hierarchical particle filter for bearings-only tracking

机译:分层粒子过滤器仅用于轴承跟踪

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We address here the classical bearings-only tracking problem (BOT) for a single target, an issue that belongs to the general class of nonlinear filtering problems. Recently, algorithm-based sequential Monte-Carlo methods (particle filtering) have been proposed. However, Fearnhead has observed that in practice this algorithm diverges. This problem is investigated further here. We show that this phenomenon is due to the unobservability of the distance between the observer and the target. We propose a new algorithm named hierarchical particle filter which takes into account this aspect of the BOT. We demonstrate that this novel filter architecture largely overperforms the classical one. Moreover, these results are confirmed when considering highly maneuvering target scenarios. Finally, we propose a general architecture based on Monte-Carlo methods for filtering initialization, able to accommodate poor prior and complex constraints.
机译:我们在这里解决单个目标的经典纯方位跟踪问题(BOT),该问题属于非线性滤波问题的一般类别。最近,已经提出了基于算法的顺序蒙特卡洛方法(粒子滤波)。但是,费恩黑德(Fearnhead)观察到在实践中该算法有所不同。这个问题在这里进一步研究。我们表明,这种现象是由于观察者与目标之间的距离不可观察而引起的。考虑到BOT的这一方面,我们提出了一种名为分层粒子滤波器的新算法。我们证明了这种新颖的滤波器架构在很大程度上优于经典的滤波器架构。此外,这些结果在考虑高度机动的目标场景时得到了证实。最后,我们提出了一种基于蒙特卡洛方法的通用体系结构,用于过滤初始化,能够适应较差的先验条件和复杂约束。

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