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Assessment of Noise Impact on Hybrid Adaptive Computational Intelligence Multisensor Data Fusion Applied to Real-Time UAV Autonomous Navigation
IEEE Latin America Transactions ( IF 1.3 ) Pub Date : 2020-02-01 , DOI: 10.1109/tla.2020.9085283
Angelo de Carvalho Paulino , Lamartine Nogueira Frutuoso Guimaraes , Elcio Hideiti Shiguemori

Recent work have successfully employed a low-cost Multisensor Data Fusion application based on Hybrid Adaptive Computational Intelligence (HACI) — the cascaded use of Fuzzy-based Computational Intelligence algorithms. The methodology has been shown able to improve considerably the accuracy of current positioning estimation systems for real-time Unmanned Aerial Vehicle (UAV) autonomous navigation — which are not robust — reducing the error in more than 45.19%. However, HACI methodology was found to have a sensitivity to noise in some parts of the estimated trajectories and, therefore, loss of performance. The problem is that none of these recent work assesses the impact of noise present on input signals and the potential benefits of their treatment prior to fusion itself. This is the main contribution of this work. Noise treatment is performed in two approaches: noise removal and noise filtering. It has been shown that for the studied dataset, the noise has a negative impact and that the chosen techniques are capable of adequately handling the noise so as to improve the original GPS precision by almost 57%.

中文翻译:

噪声对混合自适应计算智能多传感器数据融合应用于实时无人机自主导航的影响评估

最近的工作成功地采用了基于混合自适应计算智能 (HACI) 的低成本多传感器数据融合应用程序——基于模糊的计算智能算法的级联使用。该方法已被证明能够显着提高当前用于实时无人驾驶飞行器 (UAV) 自主导航的定位估计系统的准确性——这些系统并不稳健——将误差降低了 45.19% 以上。然而,发现 HACI 方法对估计轨迹的某些部分中的噪声具有敏感性,因此会损失性能。问题在于,这些最近的工作都没有评估存在于输入信号中的噪声的影响以及在融合之前对其进行处理的潜在好处。这是这项工作的主要贡献。噪声处理有两种方法:噪声去除和噪声过滤。结果表明,对于所研究的数据集,噪声具有负面影响,并且所选择的技术能够充分处理噪声,从而将原始 GPS 精度提高近 57%。
更新日期:2020-02-01
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