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An Algorithm for Large-Scale Multitarget Tracking and Parameter Estimation

机译:一种大规模多元跟踪和参数估计的算法

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摘要

Modern tracking problems require fast, scalable, and robust solutions for tracking multiple targets from noisy sensor data. In this article, an algorithm that has linear computational complexity with respect to the number of targets and measurements is presented. The method is based on the propagation of the first two factorial cumulants of a point process. The algorithm is demonstrated for tracking a million targets in cluttered environments in the fastest time yet for any such solution. A low-computational-complexity solution to the problem of joint multitarget tracking and parameter estimation is also presented. The multitarget filtering approach utilizes a single-cluster point process method for joint multiobject estimation and parameter estimation and is shown to be more computationally efficient and robust than previous implementations.
机译:现代化的跟踪问题需要快速,可扩展和强大的解决方案,用于跟踪嘈杂的传感器数据的多个目标。 在本文中,呈现了一种具有关于目标和测量数量的线性计算复杂度的算法。 该方法基于点过程的前两个阶乘累积分子的传播。 该算法在尚不用于任何此类解决方案的最快时间内跟踪杂乱环境中的百万个目标。 还提出了对联合多点跟踪和参数估计问题的低计算 - 复杂性解决方案。 多目标滤波方法利用单簇点处理方法,用于联合多元估计和参数估计,并且被示出比以前的实现更具计算高效且坚固。

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