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A Parallel Retrodiction Algorithm for Large-Scale Multitarget Tracking

机译：大规模多重机跟踪的并行销序算法

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摘要

Kalman filter-based retrodiction plays an indispensable role in modern multitarget tracking and retrodiction (MTTR) algorithms. To this end, the Rauch-Tung-Striebel (RTS) smoother is a widely used Kalman filter-based target state smoother. With a large number of targets, MTTR algorithms, particularly with large window sizes, become very computationally intensive. If not addressed, these algorithms will not meet the requirements for tracking a large number of targets in real time. A natural approach is to parallelize these algorithms to render them useful, particularly in the context of emerging multicore platforms. However, this is nontrivial, as the governing mathematical framework of the RTS smoother, namely the dependencies between complex computations, prevents any form of parallelization. Although the MTTR component can naively be parallelized ignoring the smoothing component, the overall benefit, as we demonstrate in this article, is a fraction of the best possible benefits. In this article, by carefully reformulating the underlying mathematical framework that is necessary for retrodiction, we propose a novel, easily parallelizable RTS smoother. The proposed parallelized RTS smoother we outline in this article has best data reuse and enables the overall MTTR problem to be parallelized more efficiently. We demonstrate this on a state-of-the-art multicore processor platform using the shared-memory parallelism. Our results show that the parallel MTTR solution, which includes gating, assignment, tracking, and retrodiction, can offer nearly 150 times speed up against a fully sequential version. With excellent computational performance, our proposed RTS smoother enables very large window sizes with little or no impact on the overall performance.
机译：基于Kalman筛选器的REDRODICTION在现代多元跟踪和报告（MTTR）算法中起着不可或缺的作用。为此，Rauch-Tung-striebel（RTS）更顺畅是广泛使用的基于卡尔曼滤波器的目标状态更平滑。通过大量的目标，MTTR算法，特别是具有大窗尺的大小，变得非常重要。如果没有解决，这些算法将不符合实时跟踪大量目标的要求。自然方法是将这些算法并行化以使它们有用，特别是在新兴多核平台的背景下。然而，这是非竞争的，因为RTS的控制数学框架更顺畅，即复杂计算之间的依赖关系，防止了任何形式的并行化。虽然MTTR组件可以怠于平行化，但是忽略平滑部件，整体益处，正如我们在本文中所示的那样，是最佳效益的一小部分。在本文中，通过仔细重新重新重新格式化潜在的销制所需的数学框架，我们提出了一种新颖，易于平行化的RTS更顺畅。建议的并行化RTS更顺畅我们在本文中的概述具有最佳数据重用，并使整个MTTR问题能够更有效地并行化。我们使用共享内存并行性的最先进的多核处理器平台上演示了这一点。我们的结果表明，并行MTTR解决方案，包括门控，分配，跟踪和销制，可以提供近150倍的加速，可针对完全连续的版本。具有出色的计算性能，我们提出的RTS更加顺畅，使非常大的窗口尺寸具有很少或没有影响整体性能。

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