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A Location Technique Based on Hybrid Data Fusion used to Increase the Indoor Location Accuracy

机译:基于混合数据融合的定位技术提高室内定位精度

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Systems based on wireless sensor have been presented as an alternative to location and navigation for visually impaired in environments where GPS is not effective. However, dealing with the information obtained from these sensors may present some challenges. The difficulties described by several authors are related mainly to the oscillation of the signals, which are susceptible to numerous interfering agents such as the scene itself, the presence of people, etc. It is necessary to increase the accuracy of the location in all the techniques observed. Therefore, this paper proposes to apply data fusion of wi-fi sensors with inertial sensors to allow visually impaired to location and navigation in indoor places using aspects of healthcare like sensor based systems embedded in wearable devices. The methodology adopted was associate redundant fusion and complementary fusion to construct a database obtained from several readings of the wi-fi sensors and an inertial sensor to construct a reference position to compare the data of the test readings to this reference and indicate the reliability in an indoor location. The results show that RSSI with SNR and magnetometer with gyroscope provide high error rates with raw and isolated data, but when fused, they show a more efficient location, decreasing the error rates observed in isolated mode. The error margins between the real position and the position indicated by the system were 0,386 m, 0,336 m and 0,148 m to RSSI with SNR, inertial and fusion application, respectively.
机译:已经提出了基于无线传感器的系统,作为在GPS无效的环境中视障人士的定位和导航的替代方法。但是,处理从这些传感器获得的信息可能会带来一些挑战。几位作者描述的困难主要与信号的振荡有关,信号的振荡易受场景本身,人的存在等众多干扰因素的影响。在所有技术中都必须提高定位的准确性观测到的。因此,本文提出将Wi-Fi传感器与惯性传感器进行数据融合,以利用医疗保健方面的知识,例如嵌入可穿戴设备中的基于传感器的系统,使视觉受损的人在室内场所进行定位和导航。所采用的方法是关联冗余融合和互补融合,以构建从wi-fi传感器的多个读数获得的数据库,以及惯性传感器,以构建参考位置,以将测试读数的数据与该参考进行比较,并指出可靠性。室内位置。结果表明,带SNR的RSSI和带陀螺仪的磁力计提供了原始和隔离数据的高错误率,但是融合后,它们显示的位置更有效,从而降低了在隔离模式下观察到的错误率。实际位置与系统指示的位置之间的误差容限分别为使用SNR,惯性和融合应用的RSSI的0,386 m,0,336 m和0,148 m。

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