带标记随机有限集的多传感器多目标集中跟踪

2016 International Conference on Control, Automation and Information Sciences (ICCAIS) Pub Date : 2016-10-01 DOI:10.1109/ICCAIS.2016.7822440

B. Wei, B. Nener, Weifeng Liu, Liang Ma

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引用次数: 14

摘要

本文研究了多传感器多目标跟踪问题。主要贡献是有效地实现了多传感器δ-广义标记多伯努利(δ-GLMB)更新。为了截断多目标指数的加权和，在更新中使用排序分配算法来确定最重要的项，而不计算所有项。通过线性高斯混合模型的仿真实验验证了该算法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Centralized multi-sensor multi-target tracking with labeled random finite sets

This paper addresses the problem of multi-sensor multi-target tracking. The main contribution is an efficient implementation of the multi-sensor δ-Generalized labeled Multi-Bernoulli (δ-GLMB) update. To truncate the weighted sums of the multi-target exponentials, the ranked assignment algorithm is used in the update to determine the most important terms without computing all the terms. Simulation experiments via linear Gaussian mixture models confirm the effectiveness of the proposed algorithm.

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来源期刊

2016 International Conference on Control, Automation and Information Sciences (ICCAIS)

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