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Accuracy Enhancement of Integrated Systems Based on Intelligent Information Fusion Technology

机译:基于智能信息融合技术的综合系统精度增强

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Aims at enhancing the accuracy of land vehicular navigation systems by integrating GPS and inertial measurement units, a new data fusion method based on particle filter inspired by biological evolution is proposed. In the standard particle filter, a resampling scheme is used to decrease the degeneracy phenomenon and improve estimation performance. Unfortunately, however, it could cause the undesired the particle deprivation problem, as well. In order to overcome this problem of the particle filter, we propose a novel filtering method called the genetic filter: In the proposed filter, we embed the genetic algorithm into the particle filter and overcome the problems of the standard particle filter. The proposed scheme will enhance the estimation performance in comparison with generic Kalman filter specially in the case of facing modeling uncertainty. It will also give us more reliable solution when encountering satellite signal blockage as a probable problem in land, navigation. The results have clearly demonstrated that the novel data fusion approach would improve the guidance from the point of accuracy and robustness to the mentioned problems.
机译:旨在通过集成GPS和惯性测量单元来提高陆车辆导航系统的准确性,提出了一种基于生物进化启发的粒子滤波器的新数据融合方法。在标准颗粒滤波器中,使用重采样方案来降低退化现象并提高估计性能。然而,遗憾的是,也可能导致不期望的颗粒剥夺问题。为了克服粒子过滤器的这个问题,我们提出了一种新颖的过滤方法,称为遗传滤波器:在所提出的滤波器中,我们将遗传算法嵌入到粒子过滤器中并克服标准颗粒过滤器的问题。拟议的计划将在面向建模不确定性的情况下,与通用卡尔曼过滤器相比,提高估算性能。当遇到卫星信号堵塞作为陆地,导航中可能的问题时,它还将给我们提供更可靠的解决方案。结果已清楚地表明,新型数据融合方法将从准确性和鲁棒性提高到所提到的问题的指导。

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