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Fuzzy track-to-track association and track fusion approach in distributed multisensor-multitarget multiple-attribute environment

机译：分布式多传感器多目标多属性环境中的模糊航迹关联和航迹融合方法

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摘要

A great deal of attention is currently focused on multisensor data fusion. Multisensor data fusion combines data from multiple sensor systems to achieve improved performance and provide more inferences than could be achieved using a single sensor system. One of the most important aspects of it is track-to-track-association. This paper develops a fuzzy data fusion approach to solve the problem of track-to-track association and track fusion in distributed multisensor-multitarget multiple-attribute environments in overlapping coverage scenarios. The proposed approach uses the fuzzy clustering means algorithm to reduce the number of target tracks and associate duplicate tracks by determining the degree of membership for each target track. It uses current sensor data and the known sensor resolutions for track-to-track association, track fusion, and the selection of the most accurate sensor for tracking fused targets. Numerical results based on Monte Carlo simulations are presented. The results show that the proposed approach significantly reduces the computational complexity and achieves considerable performance improvement compared to Euclidean clustering. We also show that the performance of the proposed approach is reasonable close to the performance of the Bayesian minimum mean square error criterion.
机译：当前，大量关注集中在多传感器数据融合上。多传感器数据融合结合了来自多个传感器系统的数据，以实现改进的性能并提供比使用单个传感器系统所能实现的更多推断。它的最重要方面之一是轨道到轨道的关联。本文提出了一种模糊数据融合方法，以解决重叠覆盖场景下分布式多传感器-多目标多属性环境中的航迹关联和航迹融合问题。所提出的方法使用模糊聚类均值算法来减少目标磁道的数量，并通过确定每个目标磁道的隶属度来关联重复磁道。它使用当前的传感器数据和已知的传感器分辨率进行轨迹间关联，轨迹融合以及选择最精确的传感器来跟踪融合目标。给出了基于蒙特卡洛模拟的数值结果。结果表明，与欧几里得聚类相比，该方法显着降低了计算复杂度并实现了可观的性能提升。我们还表明，所提出方法的性能与贝叶斯最小均方误差准则的性能合理接近。

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