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Time-matching extended target probability hypothesis density filter for multi-target tracking of high resolution radar

机译：高分辨率雷达多目标跟踪的时间匹配扩展目标概率假设密度滤波器

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摘要

Extended target probability hypothesis density (ET-PHD) filters have recently become popular owing to their relatively simple recursion processes, which makes them suitable for use in applications requiring real-time results, such as radar multi-target tracking. However, in the classic ET-PHD filters, the measurements of different targets are generally considered to be generated at the end of each scan. With this assumption, the measurement time diversity in radar applications cannot be modeled. To address this shortcoming, a novel time-matching ET-PHD filter in which a multi-prediction filtering framework is applied, and the true measurement times are used in the PHD propagation of each cell, i.e., prediction and correction, is proposed in this paper. In addition, a pre-partitioning strategy is employed to reduce the computational complexity of the proposed filter. The results of simulations conducted using the gamma Gaussian inverse Wishart PHD filter indicate that the proposed pre-partitioning-based time-matching ET-PHD filter is superior to standard filters in terms of both estimation accuracy and real-time performance. (C) 2018 Elsevier B.V. All rights reserved.
机译：扩展目标概率假设密度（ET-PHD）过滤器由于其相对简单的递归过程而最近变得很流行，这使其适合用于需要实时结果的应用中，例如雷达多目标跟踪。但是，在经典的ET-PHD滤波器中，通常将不同目标的测量值视为在每次扫描结束时生成。基于此假设，无法对雷达应用中的测量时间分集建模。针对这一缺点，提出了一种新颖的时间匹配ET-PHD滤波器，其中应用了多预测滤波框架，并且在每个小区的PHD传播中使用了真实的测量时间，即预测和校正。纸。另外，采用预划分策略来减少所提出的滤波器的计算复杂度。使用伽马高斯逆Wishart逆PHD滤波器进行的仿真结果表明，所提出的基于预划分的时间匹配ET-PHD滤波器在估计精度和实时性能方面均优于标准滤波器。 （C）2018 Elsevier B.V.保留所有权利。

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