GM-PHD滤波器的最优层次算法平均融合

IF 3.2 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Signal Processing Letters Pub Date : 2025-03-24 DOI:10.1109/LSP.2025.3554142

Xue Yu;Feng Xi-An

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引用次数: 0

摘要

我们以分层结构实现了高斯混杂概率假设密度（GM-PHD）滤波器的最佳算术平均（AA）融合算法。首先，推导出最优的单目标估计融合算法，在这一过程中，先验估计是不可或缺的。然后，将推导出的最优估计融合作为 AA 融合的合并方法。我们引入了一个专门计算先验密度的主滤波器，因此我们的融合算法具有分层结构。实验结果证明，我们的算法是最优的，而且优于标准 AA 融合算法。

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An Optimal Hierarchical Arithmetic Average Fusion of GM-PHD Filters

We achieve the optimal Arithmetic Average (AA) fusion algorithm of Gaussian Mixture Probability Hypothesis Density (GM-PHD) filters in a hierarchical structure. First, the optimal single-target estimate fusion is derived, during which the prior estimate is indispensable. Then, the derived optimal estimate fusion is employed as the merging method of the AA fusion. A master filter dedicated to computing prior density is introduced, so our fusion algorithm features a hierarchical structure. Experiment results evidence our algorithm's optimality and superiority over the standard AA fusion.

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来源期刊

IEEE Signal Processing Letters 工程技术-工程：电子与电气

CiteScore

7.40

自引率

12.80%

发文量

339

审稿时长

2.8 months

期刊介绍： The IEEE Signal Processing Letters is a monthly, archival publication designed to provide rapid dissemination of original, cutting-edge ideas and timely, significant contributions in signal, image, speech, language and audio processing. Papers published in the Letters can be presented within one year of their appearance in signal processing conferences such as ICASSP, GlobalSIP and ICIP, and also in several workshop organized by the Signal Processing Society.