Multi-sensor tracking with non-overlapping field for the GLMB filter

Weifeng Liu, Yimei Chen, Hailong Cui, Quanbo Ge
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引用次数: 11

Abstract

In this paper, we consider multi-sensor with non-overlapping radar field of view in the framework of labeled random finite sets (L-RFS). In this case, a target may be simultaneously observed by some of the sensors, or even none sensor. It is different from the existing assumption of all sensors with the same fields in tracking community. We first describe the field of view by modeling the detection of probability of individual sensors. Then, a multi-sensor measurement-driven of birth model is proposed. We solve this problem by using the generalized labeled multi-Bernoulli (GLMB) filter. In the final simulation, a three-target & three-sensor is given to verify the effectiveness of the proposed algorithm.
GLMB滤波器的无重叠多传感器跟踪
本文在标记随机有限集(L-RFS)的框架下,研究了具有非重叠雷达视场的多传感器。在这种情况下,一个目标可能同时被一些传感器观察到,甚至没有传感器。它不同于现有跟踪社区中所有传感器具有相同场的假设。我们首先通过对单个传感器的检测概率建模来描述视场。然后,提出了一种多传感器测量驱动的出生模型。我们利用广义标记多伯努利(GLMB)滤波器解决了这个问题。最后通过三目标三传感器的仿真验证了算法的有效性。
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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